Developing a MOOC experimentation platform: insights from a user study

Vitomir Kovanović; Charalampos Michail; Srećko Joksimović; George Siemens; Philip Katerinopoulos; Dragan Gašević

doi:10.1145/3027385.3027398

Developing a MOOC experimentation platform: insights from a user study

Vitomir Kovanović, Charalampos Michail, Srećko Joksimović, George Siemens, Philip Katerinopoulos, Dragan Gašević

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

3 Citations (Scopus)

Abstract

In 2011, the phenomenon of MOOCs had swept the world of education and put online education in the focus of the public discourse around the world. Although researchers were excited with the vast amounts of MOOC data being collected, the benefits of this data did not stand to the expectations due to several challenges. The analyses of MOOC data are very time-consuming and labor-intensive, and require and require a highly advanced set of technical skills, often not available to the education researchers. Because of this MOOC data analyses are rarely done before the courses end, limiting the potential of data to impact the student learning outcomes and experience. In this paper we introduce MOOCito (MOOC intervention tool), a user-friendly software platform for the analysis of MOOC data, that focuses on conducting data-informed instructional interventions and course experimentations. We cover important design principles behind MOOCito and provide an overview of the trends in MOOC research leading to its development. Although a work-inprogress, in this paper, we outline the prototype of MOOCito and the results of a user evaluation study that focused on system's perceived usability and ease-of-use. The results of the study are discussed, as well as their practical implications.

Original language	English
Title of host publication	LAK '17 Conference Proceedings
Subtitle of host publication	the Seventh International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data
Editors	Inge Molenaar, Xavier Ochoa, Shane Dawson
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	1-5
Number of pages	5
ISBN (Print)	9781450348706
DOIs	https://doi.org/10.1145/3027385.3027398
Publication status	Published - 13 Mar 2017
Externally published	Yes
Event	International Learning Analytics & Knowledge Conference 2017 - Morris J Wosk Centre for Dialogue, Simon Fraser University, Vancouver, Canada Duration: 13 Mar 2017 → 17 Mar 2017 Conference number: 7th http://lak17.solaresearch.org/

Conference

Conference	International Learning Analytics & Knowledge Conference 2017
Abbreviated title	LAK 2017
Country/Territory	Canada
City	Vancouver
Period	13/03/17 → 17/03/17
Internet address	http://lak17.solaresearch.org/

Keywords

A/B testing
Analysis platform
Controlled experiments
Coursera
MOOCs
Technology acceptance model
User study

Access to Document

10.1145/3027385.3027398

Cite this

Kovanović, V., Michail, C., Joksimović, S., Siemens, G., Katerinopoulos, P., & Gašević, D. (2017). Developing a MOOC experimentation platform: insights from a user study. In I. Molenaar, X. Ochoa, & S. Dawson (Eds.), LAK '17 Conference Proceedings: the Seventh International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (pp. 1-5). Association for Computing Machinery (ACM). https://doi.org/10.1145/3027385.3027398

Kovanović, Vitomir ; Michail, Charalampos ; Joksimović, Srećko et al. / Developing a MOOC experimentation platform : insights from a user study. LAK '17 Conference Proceedings: the Seventh International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. editor / Inge Molenaar ; Xavier Ochoa ; Shane Dawson. New York NY USA : Association for Computing Machinery (ACM), 2017. pp. 1-5

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abstract = "In 2011, the phenomenon of MOOCs had swept the world of education and put online education in the focus of the public discourse around the world. Although researchers were excited with the vast amounts of MOOC data being collected, the benefits of this data did not stand to the expectations due to several challenges. The analyses of MOOC data are very time-consuming and labor-intensive, and require and require a highly advanced set of technical skills, often not available to the education researchers. Because of this MOOC data analyses are rarely done before the courses end, limiting the potential of data to impact the student learning outcomes and experience. In this paper we introduce MOOCito (MOOC intervention tool), a user-friendly software platform for the analysis of MOOC data, that focuses on conducting data-informed instructional interventions and course experimentations. We cover important design principles behind MOOCito and provide an overview of the trends in MOOC research leading to its development. Although a work-inprogress, in this paper, we outline the prototype of MOOCito and the results of a user evaluation study that focused on system's perceived usability and ease-of-use. The results of the study are discussed, as well as their practical implications.",

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year = "2017",

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Kovanović, V, Michail, C, Joksimović, S, Siemens, G, Katerinopoulos, P & Gašević, D 2017, Developing a MOOC experimentation platform: insights from a user study. in I Molenaar, X Ochoa & S Dawson (eds), LAK '17 Conference Proceedings: the Seventh International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery (ACM), New York NY USA, pp. 1-5, International Learning Analytics & Knowledge Conference 2017, Vancouver, British Columbia, Canada, 13/03/17. https://doi.org/10.1145/3027385.3027398

Developing a MOOC experimentation platform: insights from a user study. / Kovanović, Vitomir; Michail, Charalampos; Joksimović, Srećko et al.
LAK '17 Conference Proceedings: the Seventh International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ed. / Inge Molenaar; Xavier Ochoa; Shane Dawson. New York NY USA: Association for Computing Machinery (ACM), 2017. p. 1-5.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Developing a MOOC experimentation platform

T2 - International Learning Analytics & Knowledge Conference 2017

AU - Kovanović, Vitomir

AU - Michail, Charalampos

AU - Joksimović, Srećko

AU - Siemens, George

AU - Katerinopoulos, Philip

AU - Gašević, Dragan

N1 - Conference code: 7th

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Y1 - 2017/3/13

N2 - In 2011, the phenomenon of MOOCs had swept the world of education and put online education in the focus of the public discourse around the world. Although researchers were excited with the vast amounts of MOOC data being collected, the benefits of this data did not stand to the expectations due to several challenges. The analyses of MOOC data are very time-consuming and labor-intensive, and require and require a highly advanced set of technical skills, often not available to the education researchers. Because of this MOOC data analyses are rarely done before the courses end, limiting the potential of data to impact the student learning outcomes and experience. In this paper we introduce MOOCito (MOOC intervention tool), a user-friendly software platform for the analysis of MOOC data, that focuses on conducting data-informed instructional interventions and course experimentations. We cover important design principles behind MOOCito and provide an overview of the trends in MOOC research leading to its development. Although a work-inprogress, in this paper, we outline the prototype of MOOCito and the results of a user evaluation study that focused on system's perceived usability and ease-of-use. The results of the study are discussed, as well as their practical implications.

AB - In 2011, the phenomenon of MOOCs had swept the world of education and put online education in the focus of the public discourse around the world. Although researchers were excited with the vast amounts of MOOC data being collected, the benefits of this data did not stand to the expectations due to several challenges. The analyses of MOOC data are very time-consuming and labor-intensive, and require and require a highly advanced set of technical skills, often not available to the education researchers. Because of this MOOC data analyses are rarely done before the courses end, limiting the potential of data to impact the student learning outcomes and experience. In this paper we introduce MOOCito (MOOC intervention tool), a user-friendly software platform for the analysis of MOOC data, that focuses on conducting data-informed instructional interventions and course experimentations. We cover important design principles behind MOOCito and provide an overview of the trends in MOOC research leading to its development. Although a work-inprogress, in this paper, we outline the prototype of MOOCito and the results of a user evaluation study that focused on system's perceived usability and ease-of-use. The results of the study are discussed, as well as their practical implications.

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KW - Analysis platform

KW - Controlled experiments

KW - Coursera

KW - MOOCs

KW - Technology acceptance model

KW - User study

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DO - 10.1145/3027385.3027398

M3 - Conference Paper

AN - SCOPUS:85016465684

SN - 9781450348706

SP - 1

EP - 5

BT - LAK '17 Conference Proceedings

A2 - Molenaar, Inge

A2 - Ochoa, Xavier

A2 - Dawson, Shane

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CY - New York NY USA

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ER -

Kovanović V, Michail C, Joksimović S, Siemens G, Katerinopoulos P, Gašević D. Developing a MOOC experimentation platform: insights from a user study. In Molenaar I, Ochoa X, Dawson S, editors, LAK '17 Conference Proceedings: the Seventh International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. New York NY USA: Association for Computing Machinery (ACM). 2017. p. 1-5 doi: 10.1145/3027385.3027398

Developing a MOOC experimentation platform: insights from a user study

Abstract

Conference

Keywords

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慕课试验平台的研发: 一项用户研究的结果

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