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MOOCs' Recommendation Based on Forum Latent Dirichlet Allocation

Published: 18 October 2018 Publication History

Abstract

Open to all, easy enrollment process and free admission make the MOOCs (Massive Open Online Course) attract a large number of learners, but few of them finish their courses and eventually obtain their certification. Several proposals are discussed, but as long as the economic model of MOOCs isn't well defined yet, certain institutions and companies keep attracting more learners to enroll and stay in their MOOCs without considering their prerequisites and needs. The aim of our contribution is to recommend enrollment in the MOOCs primarily to learners who have expressed a need or a gap and having prerequisites (i.e. in discussion forums); using LDA Topic Modeling on their shared texts we group the learners according to similar needs and interests, in order to propose for them a useful MOOC.

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ICSDE'18: Proceedings of the 2nd International Conference on Smart Digital Environment
October 2018
214 pages
ISBN:9781450365079
DOI:10.1145/3289100
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • University of Houston

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 October 2018

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Author Tags

  1. Dirichlet
  2. LDA
  3. MOOC
  4. Online discussion forum
  5. Topic Models
  6. probabilistic
  7. recommendation

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  • Refereed limited

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ICSDE'18

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ICSDE'18 Paper Acceptance Rate 32 of 80 submissions, 40%;
Overall Acceptance Rate 68 of 219 submissions, 31%

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