Predicting success: how learners' prior knowledge, skills and activities predict MOOC performance

Gregor Kennedy; Paula De Barba; Carleton Coffrin; Linda Corrin

doi:10.1145/2723576.2723593

Predicting success: how learners' prior knowledge, skills and activities predict MOOC performance

Gregor Kennedy
, Paula De Barba
, Carleton Coffrin
, Linda Corrin

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

105 Citations (Scopus)

Abstract

While MOOCs have taken the world by storm, questions remain about their pedagogical value and high rates of attrition. In this paper we argue that MOOCs which have open entry and open curriculum structures, place pressure on learners to not only have the requisite knowledge and skills to complete the course, but also the skills to traverse the course in adaptive ways that lead to success. The empirical study presented in the paper investigated the degree to which students' prior knowledge and skills, and their engagement with the MOOC as measured through learning analytics, predict end-of-MOOC performance. The findings indicate that prior knowledge is the most significant predictor of MOOC success followed by students' ability to revise and revisit their previous work.

Original language	English
Title of host publication	Proceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015
Publisher	Association for Computing Machinery (ACM)
Pages	136-140
Number of pages	5
ISBN (Electronic)	9781450334174
DOIs	https://doi.org/10.1145/2723576.2723593
Publication status	Published - 16 Mar 2015
Externally published	Yes
Event	International Learning Analytics & Knowledge Conference 2015: Scaling Up: Big Data to Big Impact - Marist College, Poughkeepsie, United States of America Duration: 16 Mar 2015 → 20 Mar 2015 Conference number: 5th https://web.archive.org/web/20150328092339/http://lak15.solaresearch.org/home https://dl.acm.org/doi/proceedings/10.1145/2723576 (Conference proceedings)

Conference

Conference	International Learning Analytics & Knowledge Conference 2015
Abbreviated title	LAK 2015
Country/Territory	United States of America
City	Poughkeepsie
Period	16/03/15 → 20/03/15
Internet address	https://web.archive.org/web/20150328092339/http://lak15.solaresearch.org/home https://dl.acm.org/doi/proceedings/10.1145/2723576 (Conference proceedings)

Keywords

Engagement
Learning analytics
MOOCs
Prior knowledge

Access to Document

10.1145/2723576.2723593

Cite this

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title = "Predicting success: how learners' prior knowledge, skills and activities predict MOOC performance",

abstract = "While MOOCs have taken the world by storm, questions remain about their pedagogical value and high rates of attrition. In this paper we argue that MOOCs which have open entry and open curriculum structures, place pressure on learners to not only have the requisite knowledge and skills to complete the course, but also the skills to traverse the course in adaptive ways that lead to success. The empirical study presented in the paper investigated the degree to which students' prior knowledge and skills, and their engagement with the MOOC as measured through learning analytics, predict end-of-MOOC performance. The findings indicate that prior knowledge is the most significant predictor of MOOC success followed by students' ability to revise and revisit their previous work.",

keywords = "Engagement, Learning analytics, MOOCs, Prior knowledge",

author = "Gregor Kennedy and \{De Barba\}, Paula and Carleton Coffrin and Linda Corrin",

year = "2015",

month = mar,

day = "16",

doi = "10.1145/2723576.2723593",

language = "English",

pages = "136--140",

booktitle = "Proceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

note = "International Learning Analytics \& Knowledge Conference 2015 : Scaling Up: Big Data to Big Impact, LAK 2015 ; Conference date: 16-03-2015 Through 20-03-2015",

url = "https://web.archive.org/web/20150328092339/http://lak15.solaresearch.org/home, https://dl.acm.org/doi/proceedings/10.1145/2723576",

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Kennedy, G, De Barba, P, Coffrin, C & Corrin, L 2015, Predicting success: how learners' prior knowledge, skills and activities predict MOOC performance. in Proceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015. Association for Computing Machinery (ACM), pp. 136-140, International Learning Analytics & Knowledge Conference 2015, Poughkeepsie, New York, United States of America, 16/03/15. https://doi.org/10.1145/2723576.2723593

Predicting success: how learners' prior knowledge, skills and activities predict MOOC performance. / Kennedy, Gregor; De Barba, Paula; Coffrin, Carleton et al.
Proceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015. Association for Computing Machinery (ACM), 2015. p. 136-140.

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

TY - GEN

T1 - Predicting success

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AU - Kennedy, Gregor

AU - De Barba, Paula

AU - Coffrin, Carleton

AU - Corrin, Linda

N1 - Conference code: 5th

PY - 2015/3/16

Y1 - 2015/3/16

N2 - While MOOCs have taken the world by storm, questions remain about their pedagogical value and high rates of attrition. In this paper we argue that MOOCs which have open entry and open curriculum structures, place pressure on learners to not only have the requisite knowledge and skills to complete the course, but also the skills to traverse the course in adaptive ways that lead to success. The empirical study presented in the paper investigated the degree to which students' prior knowledge and skills, and their engagement with the MOOC as measured through learning analytics, predict end-of-MOOC performance. The findings indicate that prior knowledge is the most significant predictor of MOOC success followed by students' ability to revise and revisit their previous work.

AB - While MOOCs have taken the world by storm, questions remain about their pedagogical value and high rates of attrition. In this paper we argue that MOOCs which have open entry and open curriculum structures, place pressure on learners to not only have the requisite knowledge and skills to complete the course, but also the skills to traverse the course in adaptive ways that lead to success. The empirical study presented in the paper investigated the degree to which students' prior knowledge and skills, and their engagement with the MOOC as measured through learning analytics, predict end-of-MOOC performance. The findings indicate that prior knowledge is the most significant predictor of MOOC success followed by students' ability to revise and revisit their previous work.

KW - Engagement

KW - Learning analytics

KW - MOOCs

KW - Prior knowledge

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M3 - Conference Paper

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PB - Association for Computing Machinery (ACM)

Y2 - 16 March 2015 through 20 March 2015

ER -

Predicting success: how learners' prior knowledge, skills and activities predict MOOC performance

Abstract

Conference

Keywords

Access to Document

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