poster

Informing the Design of Collaborative Activities in MOOCs using Actionable Predictions

Authors:

Erkan Er,

Eduardo Gómez-Sánchez,

Miguel L. Bote-Lorenzo,

Juan I. Asensio-Pérez,

Yannis DimitriadisAuthors Info & Claims

L@S '19: Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale

Article No.: 27, Pages 1 - 4

https://doi.org/10.1145/3330430.3333640

Published: 24 June 2019 Publication History

Get Access

Abstract

With the aim of supporting instructional designers in setting up collaborative learning activities in MOOCs, this paper derives prediction models for student participation in group discussions. The salient feature of these models is that they are built using only data prior to the learning activity, and can thus provide actionable predictions, as opposed to post-hoc approaches common in the MOOC literature. Some learning design scenarios that make use of this actionable information are illustrated.

References

[1]

C. Alario-Hoyos, M. Perez-Sanagustin, C. Delgado-Kloos, H.A. Parada, and M. Muñoz Organero. 2014. Delving into participants' profiles and use of social tools in MOOCs. IEEE Transactions on Learning Technologies 7, 3 (2014), 260--266.

Crossref

Google Scholar

[2]

M.L. Bote-Lorenzo and E. Gómez-Sánchez. 2018. An approach to build in situ models for the prediction of the decrease of academic engagement indicators in Massive Open Online Courses. Journal of Universal Computer Science 24, 8 (2018), 1052--1071.

Google Scholar

[3]

S. Boyer and K. Veeramachaneni. 2015. Transfer learning for predictive models in Massive Open Online Courses. In Proc. of the 17th Conf. on Artificial Intelligence in Education. 54--63.

Google Scholar

[4]

P. Dillenbourg. 1999. Collaborative Learning: Cognitive and Computational Approaches. Elsevier Science.

Google Scholar

[5]

M. Ding, Y. Wang, E. Hemberg, and U. O'Reilly. 2019. Transfer Learning Using Representation Learning in Massive Open Online Courses. In Proc. of the 9th Int. Conf. on Learning Analytics & Knowledge (LAK19). ACM, New York, NY, USA, 145--154.

Digital Library

Google Scholar

[6]

E. Er, E. Gómez-Sánchez, M.L. Bote-Lorenzo, J.I. Asensio-Pérez, and Y. Dimitriadis. 2018. Supporting group formation in ongoing MOOCs using actionable predictive models. In Proceedings of the 5th Learning with MOOCs Conference.

Google Scholar

[7]

J. Gardner and C. Brooks. 2018. Student success prediction in MOOCs. User Modeling and User-Adapted Interaction 28, 2 (2018), 127--203.

Digital Library

Google Scholar

[8]

K.F. Hew and W.S. Cheung. 2014. Students' and instructors' use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review 12 (2014), 45--58.

Crossref

Google Scholar

[9]

L.A. Jeni, J.F. Cohn, and F. Torre. 2013. Facing imbalanced data recommendations for the use of performance metrics. In Proc. of the 5th Conf. on Affective Computing and Intelligent Interaction. 245--251.

Digital Library

Google Scholar

[10]

K. Manathunga and D. Hernández-Leo. 2015. Has research on collaborative learning technologies addressed massiveness? A literature review. Educational Technology & Society 18, 4 (2015), 357--370.

Google Scholar

[11]

L. Sanz-Martínez, E. Er, A. Martínez-Monés, Y. Dimitriadis, and M.L. Bote-Lorenzo. 2019. Creating collaborative groups in a MOOC: a homogeneous engagement grouping approach. Behaviour & Information Technology (2019).

Google Scholar

[12]

H. Spoelstra, P.V. Rosmalen, T. Houtmans, and P. Sloep. 2015. Team formation instruments to enhance learner interactions in open learning environments. Computers in Human Behavior 45, 2015 (2015), 11--20.

Digital Library

Google Scholar

[13]

J. Whitehill, K. Mohan, D. Seaton, Y. Rosen, and D. Tingley. 2017. MOOC dropout prediction: How to measure accuracy?. In Proc. of the 4th ACM Conf. on Learning @ Scale. 161--164.

Digital Library

Google Scholar

[14]

Z. Zheng, T. Vogelsang, and N. Pinkwart. 2015. The impact of small learning group composition on student engagement and success in a MOOC. In Proc. of the 8th Int. Conf. on Educational Data Mining. 500--503.

Google Scholar

Cited By

View all

Gao XZhang Y(2020)Online Case Intelligent Interaction System based on Virtual Reality Technology Under the Background of Novel Coronavirus2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)10.1109/ICOEI48184.2020.9143039(862-865)Online publication date: Jun-2020
https://doi.org/10.1109/ICOEI48184.2020.9143039

Index Terms

Informing the Design of Collaborative Activities in MOOCs using Actionable Predictions
1. Applied computing
  1. Education
    1. Collaborative learning
    2. E-learning
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

Recommendations

Crowd-sourced learning in MOOCs: learning analytics meets measurement theory
LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge

This paper illustrated the promise of the combination of measurement theory and learning analytics for understanding effective MOOC learning. It reports findings from a study of whether and how MOOC log file data can assist in understanding how MOOC ...
Team based assignments in MOOCs: results and observations
L@S '18: Proceedings of the Fifth Annual ACM Conference on Learning at Scale

Teamwork and collaborative learning are considered superior to learning individually by many instructors and didactical theories. Particularly, in the context of e-learning and Massive Open Online Courses (MOOCs) we see great benefits but also great ...
Supportiveness of language MOOCs for self-regulated learning: a review of commercial language MOOCs on the market

MOOCs have been frequently applied as an effective approach to language education, especially when they can support self-regulated learning. However, few studies have discussed the supportiveness of using MOOCs for language education (i.e. language MOOCs) ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

L@S '19: Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale

June 2019

386 pages

ISBN:9781450368049

DOI:10.1145/3330430

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2019

Check for updates

Author Tags

Qualifiers

Poster
Research
Refereed limited

Funding Sources

Agencia Estatal de Investigación
Consejería de Educación, Junta de Castilla y León
Horizon 2020 Framework Programme

Conference

L@S '19

L@S '19: Sixth (2019) ACM Conference on Learning @ Scale

June 24 - 25, 2019

IL, Chicago, USA

Acceptance Rates

L@S '19 Paper Acceptance Rate 24 of 70 submissions, 34%;

Overall Acceptance Rate 117 of 440 submissions, 27%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
163
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Gao XZhang Y(2020)Online Case Intelligent Interaction System based on Virtual Reality Technology Under the Background of Novel Coronavirus2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)10.1109/ICOEI48184.2020.9143039(862-865)Online publication date: Jun-2020
https://doi.org/10.1109/ICOEI48184.2020.9143039

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Crowd-sourced learning in MOOCs: learning analytics meets measurement theory

Team based assignments in MOOCs: results and observations

Supportiveness of language MOOCs for self-regulated learning: a review of commercial language MOOCs on the market