Standard
Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features. / Spikol, Daniel; Ruffaldi, Emanuele; Landolfi, Lorenzo; Cukurova, Mutlu.
Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017. red. / Ronghuai Huang; Radu Vasiu; Kinshuk; Demetrios G Sampson; Nian-Shing Chen; Maiga Chang. Institute of Electrical and Electronics Engineers Inc., 2017. s. 269-273 8001779 (Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Spikol, D, Ruffaldi, E, Landolfi, L & Cukurova, M 2017,
Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features. i R Huang, R Vasiu, Kinshuk, DG Sampson, N-S Chen & M Chang (red),
Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017., 8001779, Institute of Electrical and Electronics Engineers Inc., Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017, s. 269-273, 17th IEEE International Conference on Advanced Learning Technologies, ICALT 2017, Timisoara, Rumænien,
03/07/2017.
https://doi.org/10.1109/ICALT.2017.122
APA
Spikol, D., Ruffaldi, E., Landolfi, L., & Cukurova, M. (2017).
Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features. I R. Huang, R. Vasiu, Kinshuk, D. G. Sampson, N-S. Chen, & M. Chang (red.),
Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017 (s. 269-273). [8001779] Institute of Electrical and Electronics Engineers Inc.. Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017
https://doi.org/10.1109/ICALT.2017.122
Vancouver
Spikol D, Ruffaldi E, Landolfi L, Cukurova M.
Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features. I Huang R, Vasiu R, Kinshuk, Sampson DG, Chen N-S, Chang M, red., Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017. Institute of Electrical and Electronics Engineers Inc. 2017. s. 269-273. 8001779. (Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017).
https://doi.org/10.1109/ICALT.2017.122
Author
Spikol, Daniel ; Ruffaldi, Emanuele ; Landolfi, Lorenzo ; Cukurova, Mutlu. / Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features. Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017. red. / Ronghuai Huang ; Radu Vasiu ; Kinshuk ; Demetrios G Sampson ; Nian-Shing Chen ; Maiga Chang. Institute of Electrical and Electronics Engineers Inc., 2017. s. 269-273 (Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017).
Bibtex
@inproceedings{892e93f18bad4b22ae7e21147516472f,
title = "Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features",
abstract = "Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). We processed and extracted different aspects of the students' interactions to answer the following question: which features of student group work are good predictors of team success in open-ended tasks with physical computing? The answer to the question provides ways to automatically identify the students' performance during the learning activities.",
keywords = "collaborative learning, Multimodal learning analytics, practice-based learning",
author = "Daniel Spikol and Emanuele Ruffaldi and Lorenzo Landolfi and Mutlu Cukurova",
year = "2017",
month = aug,
day = "3",
doi = "10.1109/ICALT.2017.122",
language = "English",
series = "Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017",
pages = "269--273",
editor = "Ronghuai Huang and Radu Vasiu and Kinshuk and Sampson, {Demetrios G} and Nian-Shing Chen and Maiga Chang",
booktitle = "Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
note = "17th IEEE International Conference on Advanced Learning Technologies, ICALT 2017 ; Conference date: 03-07-2017 Through 07-07-2017",
}
RIS
TY - GEN
T1 - Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features
AU - Spikol, Daniel
AU - Ruffaldi, Emanuele
AU - Landolfi, Lorenzo
AU - Cukurova, Mutlu
PY - 2017/8/3
Y1 - 2017/8/3
N2 - Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). We processed and extracted different aspects of the students' interactions to answer the following question: which features of student group work are good predictors of team success in open-ended tasks with physical computing? The answer to the question provides ways to automatically identify the students' performance during the learning activities.
AB - Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). We processed and extracted different aspects of the students' interactions to answer the following question: which features of student group work are good predictors of team success in open-ended tasks with physical computing? The answer to the question provides ways to automatically identify the students' performance during the learning activities.
KW - collaborative learning
KW - Multimodal learning analytics
KW - practice-based learning
UR - http://www.scopus.com/inward/record.url?scp=85030250567&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2017.122
DO - 10.1109/ICALT.2017.122
M3 - Article in proceedings
AN - SCOPUS:85030250567
T3 - Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017
SP - 269
EP - 273
BT - Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017
A2 - Huang, Ronghuai
A2 - Vasiu, Radu
A2 - Kinshuk, null
A2 - Sampson, Demetrios G
A2 - Chen, Nian-Shing
A2 - Chang, Maiga
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Advanced Learning Technologies, ICALT 2017
Y2 - 3 July 2017 through 7 July 2017
ER -
