Stimulating collaborative activity in online social learning environments with Markov decision processes

Matthew Yee-King; Mark d’Inverno

Stimulating collaborative activity in online social learning environments with Markov decision processes

Matthew Yee-King, Mark d’Inverno

Academy of Music

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

5 Citations (Scopus)

Abstract

Our work is motivated by a belief that social learning, where a community of students interact with eachother to co-create and share knowledge, is key to our students developing 21st century skills. However, convincing students to engage in and value this kind of activity is challenging. In this paper, we employ a technique from AI research called a Markov Decision Process (MDP) to model social learning activity then to suggest interventions that might increase the activity. We describe the model and its validation in simulation and draw conclusions about the effectiveness of this approach in general. The main contributions of the paper is to (i) show how it is possible to model education data as an MDP (ii) show that the resulting decision policy succeeds in guiding the system towards goal states in simulation.

Original language	English
Title of host publication	Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016
Editors	Tiffany Barnes, Min Chi, Mingyu Feng
Publisher	International Conference on Educational Data Mining
Pages	652-653
Number of pages	2
Publication status	Published - 29 Jun 2016
Event	9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States Duration: 29 Jun 2016 → 2 Jul 2016 https://educationaldatamining.org/EDM2016/ https://www.educationaldatamining.org/EDM2016/proceedings/edm2016_proceedings.pdf

Publication series

Name	Proceedings of the International Conference on Educational Data Mining

Conference

Conference	9th International Conference on Educational Data Mining, EDM 2016
Country/Territory	United States
City	Raleigh
Period	29/06/16 → 2/07/16
Internet address	https://educationaldatamining.org/EDM2016/ https://www.educationaldatamining.org/EDM2016/proceedings/edm2016_proceedings.pdf

User-Defined Keywords

Education system modelling
MDP
MOOC
Social learning

Access to Document

https://educationaldatamining.org/EDM2016/proceedings/paper_119.pdf

Cite this

Yee-King, M., & d’Inverno, M. (2016). Stimulating collaborative activity in online social learning environments with Markov decision processes. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016 (pp. 652-653). (Proceedings of the International Conference on Educational Data Mining). International Conference on Educational Data Mining. https://educationaldatamining.org/EDM2016/proceedings/paper_119.pdf

Yee-King, Matthew ; d’Inverno, Mark. / Stimulating collaborative activity in online social learning environments with Markov decision processes. Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016. editor / Tiffany Barnes ; Min Chi ; Mingyu Feng. International Conference on Educational Data Mining, 2016. pp. 652-653 (Proceedings of the International Conference on Educational Data Mining).

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title = "Stimulating collaborative activity in online social learning environments with Markov decision processes",

abstract = "Our work is motivated by a belief that social learning, where a community of students interact with eachother to co-create and share knowledge, is key to our students developing 21st century skills. However, convincing students to engage in and value this kind of activity is challenging. In this paper, we employ a technique from AI research called a Markov Decision Process (MDP) to model social learning activity then to suggest interventions that might increase the activity. We describe the model and its validation in simulation and draw conclusions about the effectiveness of this approach in general. The main contributions of the paper is to (i) show how it is possible to model education data as an MDP (ii) show that the resulting decision policy succeeds in guiding the system towards goal states in simulation.",

keywords = "Education system modelling, MDP, MOOC, Social learning",

author = "Matthew Yee-King and Mark d{\textquoteright}Inverno",

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booktitle = "Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016",

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Yee-King, M & d’Inverno, M 2016, Stimulating collaborative activity in online social learning environments with Markov decision processes. in T Barnes, M Chi & M Feng (eds), Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016. Proceedings of the International Conference on Educational Data Mining, International Conference on Educational Data Mining, pp. 652-653, 9th International Conference on Educational Data Mining, EDM 2016, Raleigh, United States, 29/06/16. <https://educationaldatamining.org/EDM2016/proceedings/paper_119.pdf>

Stimulating collaborative activity in online social learning environments with Markov decision processes. / Yee-King, Matthew; d’Inverno, Mark.
Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016. ed. / Tiffany Barnes; Min Chi; Mingyu Feng. International Conference on Educational Data Mining, 2016. p. 652-653 (Proceedings of the International Conference on Educational Data Mining).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - d’Inverno, Mark

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AB - Our work is motivated by a belief that social learning, where a community of students interact with eachother to co-create and share knowledge, is key to our students developing 21st century skills. However, convincing students to engage in and value this kind of activity is challenging. In this paper, we employ a technique from AI research called a Markov Decision Process (MDP) to model social learning activity then to suggest interventions that might increase the activity. We describe the model and its validation in simulation and draw conclusions about the effectiveness of this approach in general. The main contributions of the paper is to (i) show how it is possible to model education data as an MDP (ii) show that the resulting decision policy succeeds in guiding the system towards goal states in simulation.

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Yee-King M, d’Inverno M. Stimulating collaborative activity in online social learning environments with Markov decision processes. In Barnes T, Chi M, Feng M, editors, Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016. International Conference on Educational Data Mining. 2016. p. 652-653. (Proceedings of the International Conference on Educational Data Mining).

Stimulating collaborative activity in online social learning environments with Markov decision processes

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