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Qualitatively exploring electronic portfolios: a text mining approach to measuring student emotion as an early warning indicator

Published: 16 March 2015 Publication History

Abstract

The collection and analysis of student-level data is quickly becoming the norm across school campuses. More and more institutions are starting to use this resource as a window into better understanding the needs of their student population. In previous work, we described the use of electronic portfolio data as a proxy to measuring student engagement, and showed how it can be predictive of student retention. This paper highlights our ongoing efforts to explore and measure the valence of positive and negative emotions in student reflections and how they can serve as an early warning indicator of student disengagement.

References

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E. Aguiar, N. V. Chawla, J. Brockman, G. A. Ambrose, and V. Goodrich. Engagement vs performance: using electronic portfolios to predict first semester engineering student retention. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge, pages 103--112. ACM, 2014.
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V. Goodrich, E. Aguiar, G. A. Ambrose, L. McWilliams, J. Brockman, and N. V. Chawla. Integration of eportfolios into rst-year experience engineering course for measuring student engagement. In Proceedings of the American Society for Engineering Education Conference, 2014.
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Y. Huang, T. Goh, and C. L. Liew. Hunting suicide notes in web 2.0 - preliminary findings. pages 517--521, Los Alamitos, 2007. IEEE.
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J. Pennebaker, M. Mehl, and K. Niederhoffer. Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54: 547--577, 2003.
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J. W. Pennebaker, C. K. Chung, M. Ireland, A. Gonzales, and R. J. Booth. The Development and Psychometric Properties of LIWC2007. Austin, Texas, 2007.
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M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12): 2544--2558, 2010.

Cited By

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  • (2019)Learning analytics techniques and visualisation with textual data for determining causes of academic failureBehaviour & Information Technology10.1080/0144929X.2019.161734939:7(808-823)Online publication date: 21-May-2019
  • (2016)EXPLORING THE USEFULNESS OF ADAPTIVE ELEARNING LABORATORY ENVIRONMENTS IN TEACHING MEDICAL SCIENCEData Mining and Learning Analytics10.1002/9781118998205.ch9(139-155)Online publication date: 15-Sep-2016

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  1. Qualitatively exploring electronic portfolios: a text mining approach to measuring student emotion as an early warning indicator

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        cover image ACM Other conferences
        LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge
        March 2015
        448 pages
        ISBN:9781450334174
        DOI:10.1145/2723576
        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: 16 March 2015

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        Author Tags

        1. affect
        2. analytic approaches & methods
        3. emotions
        4. natural language processing
        5. predictive analytics
        6. quantified self
        7. reflecting learning
        8. text mining

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        LAK '15 Paper Acceptance Rate 20 of 74 submissions, 27%;
        Overall Acceptance Rate 236 of 782 submissions, 30%

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        View all
        • (2019)Learning analytics techniques and visualisation with textual data for determining causes of academic failureBehaviour & Information Technology10.1080/0144929X.2019.161734939:7(808-823)Online publication date: 21-May-2019
        • (2016)EXPLORING THE USEFULNESS OF ADAPTIVE ELEARNING LABORATORY ENVIRONMENTS IN TEACHING MEDICAL SCIENCEData Mining and Learning Analytics10.1002/9781118998205.ch9(139-155)Online publication date: 15-Sep-2016

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