Finding traces of self-regulated learning in activity streams

Analia Ivana Cicchinelli; Eduardo Enrique Veas; Abelardo Pardo; Viktoria Pammer-Schindler; Angela Fessl; Carla Alexandra Souta Barreiros; Stefanie Lindstaedt

doi:10.1145/3170358.3170381

Finding traces of self-regulated learning in activity streams

Analia Ivana Cicchinelli, Eduardo Enrique Veas, Abelardo Pardo, Viktoria Pammer-Schindler, Angela Fessl, Carla Alexandra Souta Barreiros, Stefanie Lindstaedt

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.

Original language	English
Title of host publication	Proceeding LAK '18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge
Publisher	Association of Computing Machinery
Pages	191
Number of pages	200
ISBN (Electronic)	978-1-4503-6400-3
DOIs	https://doi.org/10.1145/3170358.3170381
Publication status	Published - Mar 2018
Event	8th International Conference on Learning Analytics and Knowledge - Sydney, Australia Duration: 5 Mar 2018 → 9 Mar 2018

Conference

Conference	8th International Conference on Learning Analytics and Knowledge
Abbreviated title	LAK '18
Country/Territory	Australia
City	Sydney
Period	5/03/18 → 9/03/18

Keywords

self regulation
learning strategies
blended learning
clickstream activity
Learning Analytics

Access to Document

10.1145/3170358.3170381

Cite this

Cicchinelli, A. I., Veas, E. E., Pardo, A., Pammer-Schindler, V., Fessl, A., Souta Barreiros, C. A., & Lindstaedt, S. (2018). Finding traces of self-regulated learning in activity streams. In Proceeding LAK '18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 191). Association of Computing Machinery. https://doi.org/10.1145/3170358.3170381

Finding traces of self-regulated learning in activity streams. / Cicchinelli, Analia Ivana; Veas, Eduardo Enrique; Pardo, Abelardo et al.
Proceeding LAK '18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge. Association of Computing Machinery, 2018. p. 191.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Cicchinelli, AI, Veas, EE, Pardo, A, Pammer-Schindler, V , Fessl, A, Souta Barreiros, CA & Lindstaedt, S 2018, Finding traces of self-regulated learning in activity streams. in Proceeding LAK '18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge. Association of Computing Machinery, pp. 191, 8th International Conference on Learning Analytics and Knowledge, Sydney, Australia, 5/03/18. https://doi.org/10.1145/3170358.3170381

@inproceedings{b4f2eaafd5bd4880b3f0c0646d8610b7,

title = "Finding traces of self-regulated learning in activity streams",

abstract = "This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.",

keywords = "self regulation, learning strategies, blended learning, clickstream activity, Learning Analytics",

author = "Cicchinelli, {Analia Ivana} and Veas, {Eduardo Enrique} and Abelardo Pardo and Viktoria Pammer-Schindler and Angela Fessl and {Souta Barreiros}, {Carla Alexandra} and Stefanie Lindstaedt",

year = "2018",

month = mar,

doi = "10.1145/3170358.3170381",

language = "English",

pages = "191",

booktitle = "Proceeding LAK '18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge",

publisher = "Association of Computing Machinery",

address = "United States",

note = "8th International Conference on Learning Analytics and Knowledge, LAK '18 ; Conference date: 05-03-2018 Through 09-03-2018",

}

TY - GEN

T1 - Finding traces of self-regulated learning in activity streams

AU - Cicchinelli, Analia Ivana

AU - Veas, Eduardo Enrique

AU - Pardo, Abelardo

AU - Pammer-Schindler, Viktoria

AU - Fessl, Angela

AU - Souta Barreiros, Carla Alexandra

AU - Lindstaedt, Stefanie

PY - 2018/3

Y1 - 2018/3

N2 - This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.

AB - This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.

KW - self regulation

KW - learning strategies

KW - blended learning

KW - clickstream activity

KW - Learning Analytics

U2 - 10.1145/3170358.3170381

DO - 10.1145/3170358.3170381

M3 - Conference paper

SP - 191

BT - Proceeding LAK '18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge

PB - Association of Computing Machinery

T2 - 8th International Conference on Learning Analytics and Knowledge

Y2 - 5 March 2018 through 9 March 2018

ER -

Finding traces of self-regulated learning in activity streams

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this