Markov decision process for MOOC users behavioral inference

Firas Jarboui; Célya Gruson-Daniel; Alain Durmus; Vincent Rocchisani; Sophie Helene Goulet Ebongue; Anneliese Depoux; Wilfried Kirschenmann; Vianney Perchet

doi:10.1007/978-3-030-19875-6_9

Markov decision process for MOOC users behavioral inference

Firas Jarboui
, Célya Gruson-Daniel
, Alain Durmus
, Vincent Rocchisani
, Sophie Helene Goulet Ebongue
, Anneliese Depoux
, Wilfried Kirschenmann
, Vianney Perchet

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

Abstract

Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students. However defining the typical behaviors as well as classifying the users accordingly is a difficult task. In this paper we suggest two methods to model MOOC users behaviour given their log data. We mold their behavior into a Markov Decision Process framework. We associate the user’s intentions with the MDP reward and argue that this allows us to classify them.

Original language	English
Title of host publication	Digital Education
Subtitle of host publication	At the MOOC Crossroads Where the Interests of Academia and Business Converge - 6th European MOOCs Stakeholders Summit, EMOOCs 2019, Proceedings
Editors	Justin Reich, Jose A. Ruiperez-Valiente, Martin Wirsing, Carlos Delgado Kloos, Mauro Calise
Publisher	Springer Verlag
Pages	70-80
Number of pages	11
ISBN (Print)	9783030198749
DOIs	https://doi.org/10.1007/978-3-030-19875-6_9
Publication status	Published - 1 Jan 2019
Externally published	Yes
Event	6th European Conference on Massive Open Online Courses, EMOOCs 2019 - Naples, Italy Duration: 20 May 2019 → 22 May 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11475 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th European Conference on Massive Open Online Courses, EMOOCs 2019
Country/Territory	Italy
City	Naples
Period	20/05/19 → 22/05/19

Keywords

Inverse Reinforcement Learning
Learning analytics
Markov Decision Process
User behaviour studies

Access to Document

10.1007/978-3-030-19875-6_9

Cite this

Jarboui, F., Gruson-Daniel, C., Durmus, A., Rocchisani, V., Goulet Ebongue, S. H., Depoux, A., Kirschenmann, W., & Perchet, V. (2019). Markov decision process for MOOC users behavioral inference. In J. Reich, J. A. Ruiperez-Valiente, M. Wirsing, C. Delgado Kloos, & M. Calise (Eds.), Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge - 6th European MOOCs Stakeholders Summit, EMOOCs 2019, Proceedings (pp. 70-80). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11475 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-19875-6_9

Jarboui, Firas ; Gruson-Daniel, Célya ; Durmus, Alain et al. / Markov decision process for MOOC users behavioral inference. Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge - 6th European MOOCs Stakeholders Summit, EMOOCs 2019, Proceedings. editor / Justin Reich ; Jose A. Ruiperez-Valiente ; Martin Wirsing ; Carlos Delgado Kloos ; Mauro Calise. Springer Verlag, 2019. pp. 70-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Markov decision process for MOOC users behavioral inference",

abstract = "Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students. However defining the typical behaviors as well as classifying the users accordingly is a difficult task. In this paper we suggest two methods to model MOOC users behaviour given their log data. We mold their behavior into a Markov Decision Process framework. We associate the user{\textquoteright}s intentions with the MDP reward and argue that this allows us to classify them.",

keywords = "Inverse Reinforcement Learning, Learning analytics, Markov Decision Process, User behaviour studies",

author = "Firas Jarboui and C{\'e}lya Gruson-Daniel and Alain Durmus and Vincent Rocchisani and \{Goulet Ebongue\}, \{Sophie Helene\} and Anneliese Depoux and Wilfried Kirschenmann and Vianney Perchet",

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doi = "10.1007/978-3-030-19875-6\_9",

language = "English",

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Jarboui, F, Gruson-Daniel, C, Durmus, A, Rocchisani, V, Goulet Ebongue, SH, Depoux, A, Kirschenmann, W & Perchet, V 2019, Markov decision process for MOOC users behavioral inference. in J Reich, JA Ruiperez-Valiente, M Wirsing, C Delgado Kloos & M Calise (eds), Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge - 6th European MOOCs Stakeholders Summit, EMOOCs 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11475 LNCS, Springer Verlag, pp. 70-80, 6th European Conference on Massive Open Online Courses, EMOOCs 2019, Naples, Italy, 20/05/19. https://doi.org/10.1007/978-3-030-19875-6_9

Markov decision process for MOOC users behavioral inference. / Jarboui, Firas; Gruson-Daniel, Célya; Durmus, Alain et al.
Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge - 6th European MOOCs Stakeholders Summit, EMOOCs 2019, Proceedings. ed. / Justin Reich; Jose A. Ruiperez-Valiente; Martin Wirsing; Carlos Delgado Kloos; Mauro Calise. Springer Verlag, 2019. p. 70-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11475 LNCS).

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

TY - GEN

T1 - Markov decision process for MOOC users behavioral inference

AU - Jarboui, Firas

AU - Gruson-Daniel, Célya

AU - Durmus, Alain

AU - Rocchisani, Vincent

AU - Goulet Ebongue, Sophie Helene

AU - Depoux, Anneliese

AU - Kirschenmann, Wilfried

AU - Perchet, Vianney

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students. However defining the typical behaviors as well as classifying the users accordingly is a difficult task. In this paper we suggest two methods to model MOOC users behaviour given their log data. We mold their behavior into a Markov Decision Process framework. We associate the user’s intentions with the MDP reward and argue that this allows us to classify them.

AB - Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students. However defining the typical behaviors as well as classifying the users accordingly is a difficult task. In this paper we suggest two methods to model MOOC users behaviour given their log data. We mold their behavior into a Markov Decision Process framework. We associate the user’s intentions with the MDP reward and argue that this allows us to classify them.

KW - Inverse Reinforcement Learning

KW - Learning analytics

KW - Markov Decision Process

KW - User behaviour studies

U2 - 10.1007/978-3-030-19875-6_9

DO - 10.1007/978-3-030-19875-6_9

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SN - 9783030198749

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 70

EP - 80

BT - Digital Education

A2 - Reich, Justin

A2 - Ruiperez-Valiente, Jose A.

A2 - Wirsing, Martin

A2 - Delgado Kloos, Carlos

A2 - Calise, Mauro

PB - Springer Verlag

T2 - 6th European Conference on Massive Open Online Courses, EMOOCs 2019

Y2 - 20 May 2019 through 22 May 2019

ER -

Jarboui F, Gruson-Daniel C, Durmus A, Rocchisani V, Goulet Ebongue SH, Depoux A et al. Markov decision process for MOOC users behavioral inference. In Reich J, Ruiperez-Valiente JA, Wirsing M, Delgado Kloos C, Calise M, editors, Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge - 6th European MOOCs Stakeholders Summit, EMOOCs 2019, Proceedings. Springer Verlag. 2019. p. 70-80. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-19875-6_9

Markov decision process for MOOC users behavioral inference

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