Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data

Daniele Di Mitri; Maren Scheffel; Hendrik Drachsler; Dirk Börner; Stefaan Ternier; Marcus Specht

doi:10.1145/3027385.3027447

Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data

Daniele Di Mitri, Maren Scheffel, Hendrik Drachsler, Dirk Börner, Stefaan Ternier, Marcus Specht

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

Original language	English
Title of host publication	LAK '17
Subtitle of host publication	Proceedings of the Seventh International Learning Analytics & Knowledge Conference
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	188-197
Number of pages	10
ISBN (Print)	9781450348706
DOIs	https://doi.org/10.1145/3027385.3027447
Publication status	Published - 13 Mar 2017

Publication series

Series	ACM International Conference Proceeding Series

Keywords

Learning Analytics
Biosensors
Wearable Enhanced Learning
Multimodal data
Machine Learning

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10.1145/3027385.3027447

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Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. (2017). Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data. In LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 188-197). Association for Computing Machinery (ACM). ACM International Conference Proceeding Series https://doi.org/10.1145/3027385.3027447

Di Mitri, Daniele ; Scheffel, Maren ; Drachsler, Hendrik et al. / Learning pulse : a machine learning approach for predicting performance in self-regulated learning using multimodal data. LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference. New York : Association for Computing Machinery (ACM), 2017. pp. 188-197 (ACM International Conference Proceeding Series).

@inproceedings{f296e935a6df499aac040c24e31d7b98,

title = "Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data",

keywords = "Learning Analytics, Biosensors, Wearable Enhanced Learning, Multimodal data, Machine Learning",

author = "{Di Mitri}, Daniele and Maren Scheffel and Hendrik Drachsler and Dirk B{\"o}rner and Stefaan Ternier and Marcus Specht",

note = "DS_Citation: Di Mitri, D., Scheffel, M., Drachsler, H., B{\"o}rner, D., Ternier, S., & Specht, M. (2017). Learning pulse: a machine approach for predicting performance in self-regulated learning using multimodal data. In M. Hatala et al. (Eds.) Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK {\textquoteright}17 (pp. 188–197). New York, NY, USA: ACM Press. http://doi.org/10.1145/3027385.3027447",

year = "2017",

month = mar,

day = "13",

doi = "10.1145/3027385.3027447",

language = "English",

isbn = "9781450348706",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery (ACM)",

pages = "188--197",

booktitle = "LAK '17",

address = "United States",

}

Di Mitri, D, Scheffel, M, Drachsler, H, Börner, D, Ternier, S & Specht, M 2017, Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data. in LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference. Association for Computing Machinery (ACM), New York, ACM International Conference Proceeding Series, pp. 188-197. https://doi.org/10.1145/3027385.3027447

Learning pulse : a machine learning approach for predicting performance in self-regulated learning using multimodal data. / Di Mitri, Daniele; Scheffel, Maren; Drachsler, Hendrik et al.

LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference. New York : Association for Computing Machinery (ACM), 2017. p. 188-197 (ACM International Conference Proceeding Series).

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

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T1 - Learning pulse

T2 - a machine learning approach for predicting performance in self-regulated learning using multimodal data

AU - Di Mitri, Daniele

AU - Scheffel, Maren

AU - Drachsler, Hendrik

AU - Börner, Dirk

AU - Ternier, Stefaan

AU - Specht, Marcus

N1 - DS_Citation: Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. (2017). Learning pulse: a machine approach for predicting performance in self-regulated learning using multimodal data. In M. Hatala et al. (Eds.) Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK ’17 (pp. 188–197). New York, NY, USA: ACM Press. http://doi.org/10.1145/3027385.3027447

PY - 2017/3/13

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KW - Learning Analytics

KW - Biosensors

KW - Wearable Enhanced Learning

KW - Multimodal data

KW - Machine Learning

U2 - 10.1145/3027385.3027447

DO - 10.1145/3027385.3027447

M3 - Conference Article in proceeding

SN - 9781450348706

T3 - ACM International Conference Proceeding Series

SP - 188

EP - 197

BT - LAK '17

PB - Association for Computing Machinery (ACM)

CY - New York

ER -

Di Mitri D, Scheffel M, Drachsler H, Börner D, Ternier S, Specht M. Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data. In LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference. New York: Association for Computing Machinery (ACM). 2017. p. 188-197. (ACM International Conference Proceeding Series). doi: 10.1145/3027385.3027447

Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data

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