Combining click-Stream data with NLP tools to better understand MOOC completion

Scott Crossley, Danielle McNamara, Luc Paquette, Ryan S. Baker, Mihai Dascalu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Citations (Scopus)

Abstract

Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language processing (NLP) analyses. However, most of these analyses have not taken full advantage of the multiple types of data available. This study combines click-stream data and NLP approaches to examine if students' on-line activity and the language they produce in the online discussion forum is predictive of successful class completion. We study this analysis in the context of a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums, in a MOOC on educational data mining. The findings indicate that a mix of clickstream data and NLP indices can predict with substantial accuracy (78%) whether students complete the MOOC. This predictive power suggests that student interaction data and language data within a MOOC can help us both to understand student retention in MOOCs and to develop automated signals of student success.

Original languageEnglish (US)
Title of host publicationLAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation
PublisherAssociation for Computing Machinery
Pages6-14
Number of pages9
Volume25-29-April-2016
ISBN (Electronic)9781450341905
DOIs
StatePublished - Apr 25 2016
Event6th International Conference on Learning Analytics and Knowledge, LAK 2016 - Edinburgh, United Kingdom
Duration: Apr 25 2016Apr 29 2016

Other

Other6th International Conference on Learning Analytics and Knowledge, LAK 2016
CountryUnited Kingdom
CityEdinburgh
Period4/25/164/29/16

Fingerprint

Students
Processing
Data mining

Keywords

  • Click-stream data
  • Educational data mining
  • Educational success
  • MOOC
  • Natural language processing
  • Predictive analytics
  • Sentiment analysis

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Crossley, S., McNamara, D., Paquette, L., Baker, R. S., & Dascalu, M. (2016). Combining click-Stream data with NLP tools to better understand MOOC completion. In LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation (Vol. 25-29-April-2016, pp. 6-14). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883931

Combining click-Stream data with NLP tools to better understand MOOC completion. / Crossley, Scott; McNamara, Danielle; Paquette, Luc; Baker, Ryan S.; Dascalu, Mihai.

LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. Vol. 25-29-April-2016 Association for Computing Machinery, 2016. p. 6-14.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Crossley, S, McNamara, D, Paquette, L, Baker, RS & Dascalu, M 2016, Combining click-Stream data with NLP tools to better understand MOOC completion. in LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. vol. 25-29-April-2016, Association for Computing Machinery, pp. 6-14, 6th International Conference on Learning Analytics and Knowledge, LAK 2016, Edinburgh, United Kingdom, 4/25/16. https://doi.org/10.1145/2883851.2883931
Crossley S, McNamara D, Paquette L, Baker RS, Dascalu M. Combining click-Stream data with NLP tools to better understand MOOC completion. In LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. Vol. 25-29-April-2016. Association for Computing Machinery. 2016. p. 6-14 https://doi.org/10.1145/2883851.2883931
Crossley, Scott ; McNamara, Danielle ; Paquette, Luc ; Baker, Ryan S. ; Dascalu, Mihai. / Combining click-Stream data with NLP tools to better understand MOOC completion. LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation. Vol. 25-29-April-2016 Association for Computing Machinery, 2016. pp. 6-14
@inproceedings{a67aa5370bea44e89395611b3730f06b,
title = "Combining click-Stream data with NLP tools to better understand MOOC completion",
abstract = "Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language processing (NLP) analyses. However, most of these analyses have not taken full advantage of the multiple types of data available. This study combines click-stream data and NLP approaches to examine if students' on-line activity and the language they produce in the online discussion forum is predictive of successful class completion. We study this analysis in the context of a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums, in a MOOC on educational data mining. The findings indicate that a mix of clickstream data and NLP indices can predict with substantial accuracy (78{\%}) whether students complete the MOOC. This predictive power suggests that student interaction data and language data within a MOOC can help us both to understand student retention in MOOCs and to develop automated signals of student success.",
keywords = "Click-stream data, Educational data mining, Educational success, MOOC, Natural language processing, Predictive analytics, Sentiment analysis",
author = "Scott Crossley and Danielle McNamara and Luc Paquette and Baker, {Ryan S.} and Mihai Dascalu",
year = "2016",
month = "4",
day = "25",
doi = "10.1145/2883851.2883931",
language = "English (US)",
volume = "25-29-April-2016",
pages = "6--14",
booktitle = "LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation",
publisher = "Association for Computing Machinery",

}

TY - GEN

T1 - Combining click-Stream data with NLP tools to better understand MOOC completion

AU - Crossley, Scott

AU - McNamara, Danielle

AU - Paquette, Luc

AU - Baker, Ryan S.

AU - Dascalu, Mihai

PY - 2016/4/25

Y1 - 2016/4/25

N2 - Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language processing (NLP) analyses. However, most of these analyses have not taken full advantage of the multiple types of data available. This study combines click-stream data and NLP approaches to examine if students' on-line activity and the language they produce in the online discussion forum is predictive of successful class completion. We study this analysis in the context of a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums, in a MOOC on educational data mining. The findings indicate that a mix of clickstream data and NLP indices can predict with substantial accuracy (78%) whether students complete the MOOC. This predictive power suggests that student interaction data and language data within a MOOC can help us both to understand student retention in MOOCs and to develop automated signals of student success.

AB - Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language processing (NLP) analyses. However, most of these analyses have not taken full advantage of the multiple types of data available. This study combines click-stream data and NLP approaches to examine if students' on-line activity and the language they produce in the online discussion forum is predictive of successful class completion. We study this analysis in the context of a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums, in a MOOC on educational data mining. The findings indicate that a mix of clickstream data and NLP indices can predict with substantial accuracy (78%) whether students complete the MOOC. This predictive power suggests that student interaction data and language data within a MOOC can help us both to understand student retention in MOOCs and to develop automated signals of student success.

KW - Click-stream data

KW - Educational data mining

KW - Educational success

KW - MOOC

KW - Natural language processing

KW - Predictive analytics

KW - Sentiment analysis

UR - http://www.scopus.com/inward/record.url?scp=84976466253&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84976466253&partnerID=8YFLogxK

U2 - 10.1145/2883851.2883931

DO - 10.1145/2883851.2883931

M3 - Conference contribution

AN - SCOPUS:84976466253

VL - 25-29-April-2016

SP - 6

EP - 14

BT - LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation

PB - Association for Computing Machinery

ER -