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Beyond supervision: Human / machine distributed learning in learning sciences research. / Kubsch, Marcus; Rosenberg, Joshua M.; Krist, Christina.

Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. Hrsg. / Erica de Vries; Yotam Hod; June Ahn. Bochum : International Society of the Learning Sciences, 2021. S. 897-898.

Publikationen: Beitrag in Sammelwerk/Konferenzband Aufsatz in KonferenzbandForschungBegutachtung

Harvard

Kubsch, M, Rosenberg, JM & Krist, C 2021, Beyond supervision: Human / machine distributed learning in learning sciences research. in E de Vries, Y Hod & J Ahn (Hrsg.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. International Society of the Learning Sciences, Bochum, S. 897-898. <https://repository.isls.org/handle/1/7609>

APA

Kubsch, M., Rosenberg, J. M., & Krist, C. (2021). Beyond supervision: Human / machine distributed learning in learning sciences research. in E. de Vries, Y. Hod, & J. Ahn (Hrsg.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021 (S. 897-898). International Society of the Learning Sciences. https://repository.isls.org/handle/1/7609

Vancouver

Kubsch M, Rosenberg JM, Krist C. Beyond supervision: Human / machine distributed learning in learning sciences research. in de Vries E, Hod Y, Ahn J, Hrsg., Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. Bochum: International Society of the Learning Sciences. 2021. S. 897-898

Author

Kubsch, Marcus ; Rosenberg, Joshua M. ; Krist, Christina. / Beyond supervision: Human / machine distributed learning in learning sciences research. Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. Hrsg. / Erica de Vries ; Yotam Hod ; June Ahn. Bochum : International Society of the Learning Sciences, 2021. S. 897-898

BibTeX

@inbook{5dcfa6129a9443d28ba9d993858d42bc,
title = "Beyond supervision: Human / machine distributed learning in learning sciences research",
abstract = "Machine learning is at the core of a new set of methodologies that are currently taking the world by storm and that have a great potential to advance research in the learning sciences. However, until now research has mostly focused on applying top-down methodologies effectively aiming at replacing humans, i.e., using supervised machine learning to automate coding processes usually carried out by humans. However, this hinges on the assumption of scale effects and transferability of trained machine learning models across populations, two assumptions that may not hold, given the affordances of the learning sciences. This paper discusses the potentials and pitfalls of supervised and unsupervised machine learning for the learning sciences. We conclude that the true potential of machine learning does not lie in replacing humans but in supporting humans so that researchers can tap into new data sources and increase the validity of their findings.",
author = "Marcus Kubsch and Rosenberg, {Joshua M.} and Christina Krist",
year = "2021",
month = jun,
day = "10",
language = "English",
pages = "897--898",
editor = "{de Vries}, Erica and Yotam Hod and June Ahn",
booktitle = "Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021",
publisher = "International Society of the Learning Sciences",

}

RIS

TY - CHAP

T1 - Beyond supervision: Human / machine distributed learning in learning sciences research

AU - Kubsch, Marcus

AU - Rosenberg, Joshua M.

AU - Krist, Christina

PY - 2021/6/10

Y1 - 2021/6/10

N2 - Machine learning is at the core of a new set of methodologies that are currently taking the world by storm and that have a great potential to advance research in the learning sciences. However, until now research has mostly focused on applying top-down methodologies effectively aiming at replacing humans, i.e., using supervised machine learning to automate coding processes usually carried out by humans. However, this hinges on the assumption of scale effects and transferability of trained machine learning models across populations, two assumptions that may not hold, given the affordances of the learning sciences. This paper discusses the potentials and pitfalls of supervised and unsupervised machine learning for the learning sciences. We conclude that the true potential of machine learning does not lie in replacing humans but in supporting humans so that researchers can tap into new data sources and increase the validity of their findings.

AB - Machine learning is at the core of a new set of methodologies that are currently taking the world by storm and that have a great potential to advance research in the learning sciences. However, until now research has mostly focused on applying top-down methodologies effectively aiming at replacing humans, i.e., using supervised machine learning to automate coding processes usually carried out by humans. However, this hinges on the assumption of scale effects and transferability of trained machine learning models across populations, two assumptions that may not hold, given the affordances of the learning sciences. This paper discusses the potentials and pitfalls of supervised and unsupervised machine learning for the learning sciences. We conclude that the true potential of machine learning does not lie in replacing humans but in supporting humans so that researchers can tap into new data sources and increase the validity of their findings.

M3 - Conference contribution (Article)

SP - 897

EP - 898

BT - Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021

A2 - de Vries, Erica

A2 - Hod, Yotam

A2 - Ahn, June

PB - International Society of the Learning Sciences

CY - Bochum

ER -

ID: 1637993