Publikationen: Beitrag in Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Begutachtung
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 Konferenzband › Forschung › Begutachtung
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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