Using multimodal learning analytics to identify aspects of collaboration in project-based learning
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Standard
Using multimodal learning analytics to identify aspects of collaboration in project-based learning. / Spikol, Daniel; Ruffaldi, Emanuele; Cukurova, Mutlu.
Making a Difference: Prioritizing Equity and Access in CSCL - 12th International Conference on Computer Supported Collaborative Learning, CSCL 2017 - Conference Proceedings. red. / Brian K. Smith; Marcela Borge; Emma Mercier; Kyu Yon Lim. International Society of the Learning Sciences (ISLS), 2017. s. 263-270 (Computer-Supported Collaborative Learning Conference, CSCL, Bind 1).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Using multimodal learning analytics to identify aspects of collaboration in project-based learning
AU - Spikol, Daniel
AU - Ruffaldi, Emanuele
AU - Cukurova, Mutlu
PY - 2017
Y1 - 2017
N2 - Collaborative learning activities are a key part of education and are part of many common teaching approaches including problem-based learning, inquiry-based learning, and project-based learning. However, in open-ended collaborative small group work where learners make unique solutions to tasks that involve robotics, electronics, programming, and design artefacts evidence on the effectiveness of using these learning activities are hard to find. The paper argues that multimodal learning analytics (MMLA) can offer novel methods that can generate unique information about what happens when students are engaged in collaborative, project-based learning activities. Through the use of multimodal learning analytics platform, we collected various streams of data, processed and extracted multimodal interactions to answer the following question: Which features of MMLA are good predictors of collaborative problem-solving in open-ended tasks in project-based learning? Manual entered scores of CPS were regressed using machine-learning methods. The answer to the question provides potential ways to automatically identify aspects of collaboration in projectbased learning.
AB - Collaborative learning activities are a key part of education and are part of many common teaching approaches including problem-based learning, inquiry-based learning, and project-based learning. However, in open-ended collaborative small group work where learners make unique solutions to tasks that involve robotics, electronics, programming, and design artefacts evidence on the effectiveness of using these learning activities are hard to find. The paper argues that multimodal learning analytics (MMLA) can offer novel methods that can generate unique information about what happens when students are engaged in collaborative, project-based learning activities. Through the use of multimodal learning analytics platform, we collected various streams of data, processed and extracted multimodal interactions to answer the following question: Which features of MMLA are good predictors of collaborative problem-solving in open-ended tasks in project-based learning? Manual entered scores of CPS were regressed using machine-learning methods. The answer to the question provides potential ways to automatically identify aspects of collaboration in projectbased learning.
UR - http://www.scopus.com/inward/record.url?scp=85051958104&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:85051958104
T3 - Computer-Supported Collaborative Learning Conference, CSCL
SP - 263
EP - 270
BT - Making a Difference
A2 - Smith, Brian K.
A2 - Borge, Marcela
A2 - Mercier, Emma
A2 - Lim, Kyu Yon
PB - International Society of the Learning Sciences (ISLS)
T2 - 12th International Conference on Computer Supported Collaborative Learning - Making a Difference: Prioritizing Equity and Access in CSCL, CSCL 2017
Y2 - 18 June 2017 through 22 June 2017
ER -
ID: 256265070