research-article

Replicating MOOC predictive models at scale

Authors:

Christopher Brooks,

Juan Miguel Andres,

Ryan BakerAuthors Info & Claims

L@S '18: Proceedings of the Fifth Annual ACM Conference on Learning at Scale

Article No.: 1, Pages 1 - 10

https://doi.org/10.1145/3231644.3231656

Published: 26 June 2018 Publication History

Abstract

We present a case study in predictive model replication for student dropout in Massive Open Online Courses (MOOCs) using a large and diverse dataset (133 sessions of 28 unique courses offered by two institutions). This experiment was run on the MOOC Replication Framework (MORF), which makes it feasible to fully replicate complex machine learned models, from raw data to model evaluation. We provide an overview of the MORF platform architecture and functionality, and demonstrate its use through a case study. In this replication of [41], we contextualize and evaluate the results of the previous work using statistical tests and a more effective model evaluation scheme. We find that only some of the original findings replicate across this larger and more diverse sample of MOOCs, with others replicating significantly in the opposite direction. Our analysis also reveals results which are highly relevant to the prediction task which were not reported in the original experiment. This work demonstrates the importance of replication of predictive modeling research in MOOCs using large and diverse datasets, illuminates the challenges of doing so, and describes our freely available, open-source software framework to overcome barriers to replication.

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cover image ACM Other conferences

L@S '18: Proceedings of the Fifth Annual ACM Conference on Learning at Scale

June 2018

391 pages

ISBN:9781450358866

DOI:10.1145/3231644

Conference Chair:
Rose Luckin
UCL Institute of Education, UK
,
Program Chairs:
Scott Klemmer
University of California at San Diego
,
Kenneth Koedinger
Carnegie Mellon University

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 26 June 2018

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L@S '18: Fifth (2018) ACM Conference on Learning @ Scale

June 26 - 28, 2018

London, United Kingdom

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L@S '18 Paper Acceptance Rate 24 of 58 submissions, 41%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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https://doi.org/10.2196/40306
Kitto KManly CFerguson RPoquet OHilliger IKhosravi HRienties BDawson S(2023)Towards more replicable content analysis for learning analyticsLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576096(303-314)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3576050.3576096
Farrow EMoore JGasevic DHilliger IKhosravi HRienties BDawson S(2023)Names, Nicknames, and Spelling Errors: Protecting Participant Identity in Learning Analytics of Online DiscussionsLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576070(145-155)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3576050.3576070
Andres-Bray JHutt SBaker RSpikol DViberg OMartínez-Monés AGuo P(2023)Exploring Cross-Country Prediction Model Generalizability in MOOCsProceedings of the Tenth ACM Conference on Learning @ Scale10.1145/3573051.3593380(183-194)Online publication date: 20-Jul-2023
https://dl.acm.org/doi/10.1145/3573051.3593380
Hu YDonald CGiacaman N(2022)A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed?International Journal of Educational Technology in Higher Education10.1186/s41239-022-00353-719:1Online publication date: 13-Sep-2022
https://doi.org/10.1186/s41239-022-00353-7
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Hutt SBaker RAshenafi MAndres‐Bray JBrooks C(2022) Controlled outputs, full data: A privacy‐protecting infrastructure for MOOC data British Journal of Educational Technology10.1111/bjet.1323153:4(756-775)Online publication date: 11-May-2022
https://doi.org/10.1111/bjet.13231
Maldonado-Mahauad JAlario-Hoyos CDelgado Kloos CPerez-Sanagustin M(2022)Adaptation of a Process Mining Methodology to Analyse Learning Strategies in a Synchronous Massive Open Online CourseInformation and Communication Technologies10.1007/978-3-031-18272-3_9(117-136)Online publication date: 5-Oct-2022
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Joksimović SMarshall RRakotoarivelo TLadjal DZhan CPardo A(2021)Privacy-Driven Learning AnalyticsManage Your Own Learning Analytics10.1007/978-3-030-86316-6_1(1-22)Online publication date: 5-Dec-2021
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