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Sentiment Analysis across the Courses of a MOOC Specialization

Published: 22 February 2019 Publication History

Abstract

A Massive Open Online Course (MOOC) is an effective way for a university to deliver course content that reaches a global audience. Such developments are not without substantial costs and risks [1]. On the Coursera platform, there are MOOC specializations, which package a sequence of related courses. Retaining learners is of particular interest to instructors as they progress through the courses of a long specialization. Learners that encounter delays, unfairness, or plagiarism in peer evaluations of assignments could become dissatisfied enough to withdraw, for example. We describe using trends in sentiment analysis of discussion forum postings across the courses. The idea is to detect, interpret, and address points of lower sentiment in a MOOC specialization, to avoid losing learners. We outline our findings with a MOOC specialization on software product management, which consists of six courses, involving a nominal 24 weeks of content [2,3]. Interestingly, higher sentiment in a course's content did not necessarily preserve enrollment numbers for the subsequent course.

References

[1]
K. Wong. Experiences in constructing a MOOC specialization. In 21st Western Canadian Conference on Computing Education (WCCCE 2016), pages 62--65, Kamloops, Canada, May 2016. ACM.
[2]
University of Alberta. Software Product Management Specialization, 2018. https://www.coursera.org/specializations/product-management.
[3]
K. Wong, M. Patzelt, B. Poulette, and R. Hathaway. Scenario-based learning in a MOOC specialization capstone on software product management. In IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE 2017), Buenos Aires, Argentina, May 2017. IEEE.

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  • (2022)Sentiment Analysis and Vector Embedding: A Comparative StudySmart Trends in Computing and Communications10.1007/978-981-16-9967-2_30(311-321)Online publication date: 6-Jul-2022

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  1. Sentiment Analysis across the Courses of a MOOC Specialization

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      cover image ACM Conferences
      SIGCSE '19: Proceedings of the 50th ACM Technical Symposium on Computer Science Education
      February 2019
      1364 pages
      ISBN:9781450358903
      DOI:10.1145/3287324
      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.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 22 February 2019

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      Author Tags

      1. coursera specialization
      2. discussion forum
      3. massive open online course
      4. mooc retention
      5. natural language processing
      6. sentiment analysis
      7. software product management

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      SIGCSE '19 Paper Acceptance Rate 169 of 526 submissions, 32%;
      Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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      • (2022)Sentiment Analysis and Vector Embedding: A Comparative StudySmart Trends in Computing and Communications10.1007/978-981-16-9967-2_30(311-321)Online publication date: 6-Jul-2022

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