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An analysis framework for collaborative problem solving in practice-based learning activities : A mixed-method approach. / Cukurova, Mutlu; Avramides, Katerina; Spikol, Daniel; Luckin, Rose; Mavrikis, Manolis.
LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. ACM Association for Computing Machinery, 2016. s. 84-88 (ACM International Conference Proceeding Series, Bind 25-29-April-2016).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Cukurova, M, Avramides, K
, Spikol, D, Luckin, R & Mavrikis, M 2016,
An analysis framework for collaborative problem solving in practice-based learning activities: A mixed-method approach. i
LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. ACM Association for Computing Machinery, ACM International Conference Proceeding Series, bind 25-29-April-2016, s. 84-88, 6th International Conference on Learning Analytics and Knowledge, LAK 2016, Edinburgh, Storbritannien,
25/04/2016.
https://doi.org/10.1145/2883851.2883900APA
Cukurova, M., Avramides, K.
, Spikol, D., Luckin, R., & Mavrikis, M. (2016).
An analysis framework for collaborative problem solving in practice-based learning activities: A mixed-method approach. I
LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation (s. 84-88). ACM Association for Computing Machinery. ACM International Conference Proceeding Series Bind 25-29-April-2016
https://doi.org/10.1145/2883851.2883900Vancouver
Cukurova M, Avramides K
, Spikol D, Luckin R, Mavrikis M.
An analysis framework for collaborative problem solving in practice-based learning activities: A mixed-method approach. I LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. ACM Association for Computing Machinery. 2016. s. 84-88. (ACM International Conference Proceeding Series, Bind 25-29-April-2016).
https://doi.org/10.1145/2883851.2883900Author
Cukurova, Mutlu ; Avramides, Katerina ; Spikol, Daniel ; Luckin, Rose ; Mavrikis, Manolis. / An analysis framework for collaborative problem solving in practice-based learning activities : A mixed-method approach. LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. ACM Association for Computing Machinery, 2016. s. 84-88 (ACM International Conference Proceeding Series, Bind 25-29-April-2016).
Bibtex
@inproceedings{0ec2ff08e93c4838bfa5b717600adbca,
title = "An analysis framework for collaborative problem solving in practice-based learning activities: A mixed-method approach",
abstract = "Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.",
keywords = "Analysis framework, Collaborative learning, Practice-based learning, Problem solving",
author = "Mutlu Cukurova and Katerina Avramides and Daniel Spikol and Rose Luckin and Manolis Mavrikis",
year = "2016",
month = apr,
day = "25",
doi = "10.1145/2883851.2883900",
language = "English",
series = "ACM International Conference Proceeding Series",
pages = "84--88",
booktitle = "LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact",
publisher = "ACM Association for Computing Machinery",
note = "6th International Conference on Learning Analytics and Knowledge, LAK 2016 ; Conference date: 25-04-2016 Through 29-04-2016",
}
RIS
TY - GEN
T1 - An analysis framework for collaborative problem solving in practice-based learning activities
T2 - 6th International Conference on Learning Analytics and Knowledge, LAK 2016
AU - Cukurova, Mutlu
AU - Avramides, Katerina
AU - Spikol, Daniel
AU - Luckin, Rose
AU - Mavrikis, Manolis
PY - 2016/4/25
Y1 - 2016/4/25
N2 - Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.
AB - Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.
KW - Analysis framework
KW - Collaborative learning
KW - Practice-based learning
KW - Problem solving
UR - http://www.scopus.com/inward/record.url?scp=84976522501&partnerID=8YFLogxK
U2 - 10.1145/2883851.2883900
DO - 10.1145/2883851.2883900
M3 - Article in proceedings
AN - SCOPUS:84976522501
T3 - ACM International Conference Proceeding Series
SP - 84
EP - 88
BT - LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact
PB - ACM Association for Computing Machinery
Y2 - 25 April 2016 through 29 April 2016
ER -
