Mar Perez-Sanagustin, Ronald Pérez-Álvarez, Jorge Maldonado-Mahauad, Esteban Villalobos, Isabel Hilliger, Josefina Hernández, Diego Sapunar, Pedro Manuel Moreno-Marcos, Pedro J. Muñoz-Merino, Carlos Delgado Kloos, and Jon Imaz. 2021. Can Feedback based on Predictive Data Improve Learners’ Passing Rates in MOOCs? A Preliminary Analysis. In Proceedings of the Eighth ACM Conference on Learning @ Scale (L@S ’21). Association for Computing Machinery, New York, NY, USA, 339342. DOI:https://doi.org/10.1145/3430895.3460991 (2021)

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

Revista : ACM Conferences
Páginas : 339-342
Tipo de publicación : Conferencia No A* Ir a publicación

Abstract

This work in progress paper investigates if timely feedback increases learners’ passing rate in a MOOC. An experiment conducted with 2,421 learners in the Coursera platform tests if weekly messages sent to groups of learners with the same probability of dropping out the course can improve retention. These messages can contain information about:(1) the average time spent in the course, or (2) the average time per learning session, or (3) the exercises performed, or (4) the video-lectures completed. Preliminary results show that the completion rate increased 12% with the intervention compared with data from 1,445 learners that participated in the same course in a previous session without the intervention. We discuss the limitations of these preliminary results and the future research derived from them.