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Understanding Peer Feedback Contributions Using Natural Language Processing. / Castro, Mayara Simões de Oliveira; Mello, Rafael Ferreira; Fiorentino, Giuseppe; Viberg, Olga; Spikol, Daniel; Baars, Martine; Gašević, Dragan.
Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings. red. / Olga Viberg; Ioana Jivet; Pedro J. Muñoz-Merino; Maria Perifanou; Tina Papathoma. Springer, 2023. s. 399-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 14200 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Castro, MSDO, Mello, RF, Fiorentino, G, Viberg, O
, Spikol, D, Baars, M & Gašević, D 2023,
Understanding Peer Feedback Contributions Using Natural Language Processing. i O Viberg, I Jivet, PJ Muñoz-Merino, M Perifanou & T Papathoma (red),
Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 14200 LNCS, s. 399-414, Proceedings of the 18th European Conference on Technology Enhanced Learning, ECTEL 2023, Aveiro, Portugal,
04/09/2023.
https://doi.org/10.1007/978-3-031-42682-7_27APA
Castro, M. S. D. O., Mello, R. F., Fiorentino, G., Viberg, O.
, Spikol, D., Baars, M., & Gašević, D. (2023).
Understanding Peer Feedback Contributions Using Natural Language Processing. I O. Viberg, I. Jivet, P. J. Muñoz-Merino, M. Perifanou, & T. Papathoma (red.),
Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings (s. 399-414). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 14200 LNCS
https://doi.org/10.1007/978-3-031-42682-7_27Vancouver
Castro MSDO, Mello RF, Fiorentino G, Viberg O
, Spikol D, Baars M o.a.
Understanding Peer Feedback Contributions Using Natural Language Processing. I Viberg O, Jivet I, Muñoz-Merino PJ, Perifanou M, Papathoma T, red., Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings. Springer. 2023. s. 399-414. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 14200 LNCS).
https://doi.org/10.1007/978-3-031-42682-7_27Author
Castro, Mayara Simões de Oliveira ; Mello, Rafael Ferreira ; Fiorentino, Giuseppe ; Viberg, Olga ; Spikol, Daniel ; Baars, Martine ; Gašević, Dragan. / Understanding Peer Feedback Contributions Using Natural Language Processing. Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings. red. / Olga Viberg ; Ioana Jivet ; Pedro J. Muñoz-Merino ; Maria Perifanou ; Tina Papathoma. Springer, 2023. s. 399-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 14200 LNCS).
Bibtex
@inproceedings{be390dd3bdae4b8e9035b29e39d0a517,
title = "Understanding Peer Feedback Contributions Using Natural Language Processing",
abstract = "Peer feedback has been widely used in computer-supported collaborative learning (CSCL) setting to improve students{\textquoteright} engagement with massive courses. Although the peer feedback process increases students{\textquoteright} self-regulatory practice, metacognition, and academic achievement, instructors need to go through large amounts of feedback text data which is much more time-consuming. To address this challenge, the present study proposes an automated content analysis approach to identify relevant categories in peer feedback based on traditional and sequence-based classifiers using TF-IDF and content-independent features. We use a data set from an extensive course (N = 231 students) in the setting of engineering higher education. In particular, a total of 2,444 peer feedback messages were analyzed. The CRF classification model based on the TF-IDF features achieved the best performance. The results illustrate that the ability to scale up the automatic analysis of peer feedback provides new opportunities for student-improved learning and improved teacher support in higher education at scale.",
keywords = "Computer Supported Collaborative Learning, Content Analysis, Higher Education, Natural Language Processing, Peer Feedback",
author = "Castro, {Mayara Sim{\~o}es de Oliveira} and Mello, {Rafael Ferreira} and Giuseppe Fiorentino and Olga Viberg and Daniel Spikol and Martine Baars and Dragan Ga{\v s}evi{\'c}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s).; Proceedings of the 18th European Conference on Technology Enhanced Learning, ECTEL 2023 ; Conference date: 04-09-2023 Through 08-09-2023",
year = "2023",
doi = "10.1007/978-3-031-42682-7_27",
language = "English",
isbn = "9783031426810",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "399--414",
editor = "Olga Viberg and Ioana Jivet and Mu{\~n}oz-Merino, {Pedro J.} and Maria Perifanou and Tina Papathoma",
booktitle = "Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Understanding Peer Feedback Contributions Using Natural Language Processing
AU - Castro, Mayara Simões de Oliveira
AU - Mello, Rafael Ferreira
AU - Fiorentino, Giuseppe
AU - Viberg, Olga
AU - Spikol, Daniel
AU - Baars, Martine
AU - Gašević, Dragan
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - Peer feedback has been widely used in computer-supported collaborative learning (CSCL) setting to improve students’ engagement with massive courses. Although the peer feedback process increases students’ self-regulatory practice, metacognition, and academic achievement, instructors need to go through large amounts of feedback text data which is much more time-consuming. To address this challenge, the present study proposes an automated content analysis approach to identify relevant categories in peer feedback based on traditional and sequence-based classifiers using TF-IDF and content-independent features. We use a data set from an extensive course (N = 231 students) in the setting of engineering higher education. In particular, a total of 2,444 peer feedback messages were analyzed. The CRF classification model based on the TF-IDF features achieved the best performance. The results illustrate that the ability to scale up the automatic analysis of peer feedback provides new opportunities for student-improved learning and improved teacher support in higher education at scale.
AB - Peer feedback has been widely used in computer-supported collaborative learning (CSCL) setting to improve students’ engagement with massive courses. Although the peer feedback process increases students’ self-regulatory practice, metacognition, and academic achievement, instructors need to go through large amounts of feedback text data which is much more time-consuming. To address this challenge, the present study proposes an automated content analysis approach to identify relevant categories in peer feedback based on traditional and sequence-based classifiers using TF-IDF and content-independent features. We use a data set from an extensive course (N = 231 students) in the setting of engineering higher education. In particular, a total of 2,444 peer feedback messages were analyzed. The CRF classification model based on the TF-IDF features achieved the best performance. The results illustrate that the ability to scale up the automatic analysis of peer feedback provides new opportunities for student-improved learning and improved teacher support in higher education at scale.
KW - Computer Supported Collaborative Learning
KW - Content Analysis
KW - Higher Education
KW - Natural Language Processing
KW - Peer Feedback
U2 - 10.1007/978-3-031-42682-7_27
DO - 10.1007/978-3-031-42682-7_27
M3 - Article in proceedings
AN - SCOPUS:85172010223
SN - 9783031426810
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 399
EP - 414
BT - Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings
A2 - Viberg, Olga
A2 - Jivet, Ioana
A2 - Muñoz-Merino, Pedro J.
A2 - Perifanou, Maria
A2 - Papathoma, Tina
PB - Springer
T2 - Proceedings of the 18th European Conference on Technology Enhanced Learning, ECTEL 2023
Y2 - 4 September 2023 through 8 September 2023
ER -
