Can Feedback based on Predictive Data Improve Learners' Passing Rates in MOOCs? A Preliminary Analysis-Reference-Cited by-同舟云学术

Can Feedback based on Predictive Data Improve Learners' Passing Rates in MOOCs? A Preliminary Analysis

Published:2021-06-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Eighth ACM Conference on Learning @ Scale
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Author:

Perez-Sanagustin Mar¹,Pérez-Álvarez Ronald²,Maldonado-Mahauad Jorge³,Villalobos Esteban⁴,Hilliger Isabel⁴,Hernández Josefina⁴,Sapunar Diego⁴,Moreno-Marcos Pedro Manuel⁵,Muñoz-Merino Pedro J.⁵,Delgado Kloos Carlos⁵,Imaz Jon⁵

Affiliation:

1. Universitat Paul Sabatier Toulouse, IRIT, Toulouse, France

2. Universidad de Costa Rica, Esperanza, Costa Rica

3. Universidad de Cuenca, Cuenca, Ecuador

4. Pontificia Universidad Católica de Chile, Santiago, Chile

5. Universidad Carlos III de Madrid, Madrid, Spain

Funder

European Commission

Agence Nationale de la Recherche

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3430895.3460991

Reference21 articles.

1. Class Central Report 2020. Retrieved March 10th 2021 from: https://www.classcentral.com/report/class-central-2020-review/ Class Central Report 2020. Retrieved March 10th 2021 from: https://www.classcentral.com/report/class-central-2020-review/

2. Topali P. etal 2020. "Houston we have a problem": Revealing MOOC practitioners' experiences regarding feedback provision to learners facing difficulties. Computer App. in Engineering Education. Topali P. et al. 2020. "Houston we have a problem": Revealing MOOC practitioners' experiences regarding feedback provision to learners facing difficulties. Computer App. in Engineering Education.

3. Factors affecting student dropout in MOOCs: a cause and effect decision‐making model

4. What do participants think of today’s MOOCs: an updated look at the benefits and challenges of MOOCs designed for working professionals

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