Studying MOOC completion at scale using the MOOC replication framework

Juan Miguel L. Andres; Ryan S. Baker; George Siemens; Catherine A. Spann; Dragan Gašević; Scott Crossley

Studying MOOC completion at scale using the MOOC replication framework

Juan Miguel L. Andres, Ryan S. Baker, George Siemens, Catherine A. Spann, Dragan Gašević, Scott Crossley

Research output: Contribution to conference › Poster › peer-review

Abstract

Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data.

Original language	English
Pages	338-339
Number of pages	2
Publication status	Published - 2017
Externally published	Yes
Event	Educational Data Mining 2017 - Central China Normal University, Wuhan, China Duration: 25 Jun 2017 → 28 Jun 2017 Conference number: 10th http://educationaldatamining.org/EDM2017/

Conference

Conference	Educational Data Mining 2017
Abbreviated title	EDM 2017
Country/Territory	China
City	Wuhan
Period	25/06/17 → 28/06/17
Internet address	http://educationaldatamining.org/EDM2017/

Access to Document

260295502-oaFinal published version, 184 KB

http://educationaldatamining.org/EDM2017/proc_files/proceedings.pdf

Studying MOOC completion at scale using the MOOC replication framework
Andres, J. M. L., Baker, R. S., Gašević, D., Siemens, G., Crossley, S. A. & Joksimović, S., 2018, Proceedings of the 8th International Conference on Learning Analytics & Knowledge (LAK'18): Towards User-Centred Learning Analytics. Buckingham Shum, S., Ferguson, R., Merceron, A. & Ochoa, X. (eds.). New York NY USA: Association for Computing Machinery (ACM), p. 71-78 8 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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Cite this

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title = "Studying MOOC completion at scale using the MOOC replication framework",

abstract = "Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data. ",

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year = "2017",

language = "English",

pages = "338--339",

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T1 - Studying MOOC completion at scale using the MOOC replication framework

AU - Andres, Juan Miguel L.

AU - Baker, Ryan S.

AU - Siemens, George

AU - Spann, Catherine A.

AU - Gašević, Dragan

AU - Crossley, Scott

N1 - Conference code: 10th

PY - 2017

Y1 - 2017

N2 - Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data.

AB - Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data.

M3 - Poster

SP - 338

EP - 339

T2 - Educational Data Mining 2017

Y2 - 25 June 2017 through 28 June 2017

ER -