Understand students’ self-reflections through learning analytics

Vitomir Kovanović; Srećko Joksimović; Negin Mirriahi; Ellen Blaine; Dragan Gašević; George Siemens; Shane Dawson

doi:10.1145/3170358.3170374

Understand students’ self-reflections through learning analytics

Vitomir Kovanović, Srećko Joksimović, Negin Mirriahi, Ellen Blaine, Dragan Gašević, George Siemens, Shane Dawson

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

46 Citations (Scopus)

Abstract

Reflective writing has been widely recognized as one of the most effective activities for fostering students’ reflective and critical thinking. The analysis of students’ reflective writings has been the focus of many research studies. However, to date this has been typically a very labor-intensive manual process involving content analysis of student writings. With recent advancements in the field of learning analytics, there have been several attempts to use text analytics to examine student reflective writings. This paper presents the results of a study examining the use of theoretically-sound linguistic indicators of different psychological processes for the development of an analytics system for assessment of reflective writing. More precisely, we developed a random-forest classification system using linguistic indicators provided by the LIWC and Coh-Metrix tools. We also examined what particular indicators are representative of the different types of student reflective writings.

Original language	English
Title of host publication	Proceedings of the 8th International Conference on Learning Analytics & Knowledge (LAK'18)
Subtitle of host publication	Towards User-Centred Learning Analytics
Editors	Simon Buckingham Shum, Rebecca Ferguson, Agathe Merceron, Xavier Ochoa
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	389-398
Number of pages	10
ISBN (Print)	9781450364003
DOIs	https://doi.org/10.1145/3170358.3170374
Publication status	Published - 7 Mar 2018
Event	International Learning Analytics & Knowledge Conference 2018 - SMC Conference & Function Centre, Sydney, Australia Duration: 5 Mar 2018 → 9 Mar 2018 Conference number: 8th https://latte-analytics.sydney.edu.au/ (Conference website) https://dl.acm.org/doi/proceedings/10.1145/3170358 (Proceedings)

Conference

Conference	International Learning Analytics & Knowledge Conference 2018
Abbreviated title	LAK 2018
Country/Territory	Australia
City	Sydney
Period	5/03/18 → 9/03/18
Internet address	https://latte-analytics.sydney.edu.au/ (Conference website) https://dl.acm.org/doi/proceedings/10.1145/3170358 (Proceedings)

Keywords

Learning analytics
Online learning
Self-reflections
Text mining

Access to Document

10.1145/3170358.3170374

Cite this

Kovanović, V., Joksimović, S., Mirriahi, N., Blaine, E., Gašević, D., Siemens, G., & Dawson, S. (2018). Understand students’ self-reflections through learning analytics. In S. B. Shum, R. Ferguson, A. Merceron, & X. Ochoa (Eds.), Proceedings of the 8th International Conference on Learning Analytics & Knowledge (LAK'18): Towards User-Centred Learning Analytics (pp. 389-398). Association for Computing Machinery (ACM). https://doi.org/10.1145/3170358.3170374

Kovanović, Vitomir ; Joksimović, Srećko ; Mirriahi, Negin et al. / Understand students’ self-reflections through learning analytics. Proceedings of the 8th International Conference on Learning Analytics & Knowledge (LAK'18): Towards User-Centred Learning Analytics. editor / Simon Buckingham Shum ; Rebecca Ferguson ; Agathe Merceron ; Xavier Ochoa. New York NY USA : Association for Computing Machinery (ACM), 2018. pp. 389-398

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title = "Understand students{\textquoteright} self-reflections through learning analytics",

abstract = "Reflective writing has been widely recognized as one of the most effective activities for fostering students{\textquoteright} reflective and critical thinking. The analysis of students{\textquoteright} reflective writings has been the focus of many research studies. However, to date this has been typically a very labor-intensive manual process involving content analysis of student writings. With recent advancements in the field of learning analytics, there have been several attempts to use text analytics to examine student reflective writings. This paper presents the results of a study examining the use of theoretically-sound linguistic indicators of different psychological processes for the development of an analytics system for assessment of reflective writing. More precisely, we developed a random-forest classification system using linguistic indicators provided by the LIWC and Coh-Metrix tools. We also examined what particular indicators are representative of the different types of student reflective writings.",

keywords = "Learning analytics, Online learning, Self-reflections, Text mining",

author = "Vitomir Kovanovi{\'c} and Sre{\'c}ko Joksimovi{\'c} and Negin Mirriahi and Ellen Blaine and Dragan Ga{\v s}evi{\'c} and George Siemens and Shane Dawson",

year = "2018",

month = mar,

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doi = "10.1145/3170358.3170374",

language = "English",

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editor = "Shum, {Simon Buckingham} and Rebecca Ferguson and Agathe Merceron and Xavier Ochoa",

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note = "International Learning Analytics & Knowledge Conference 2018, LAK 2018 ; Conference date: 05-03-2018 Through 09-03-2018",

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Kovanović, V, Joksimović, S, Mirriahi, N, Blaine, E, Gašević, D, Siemens, G & Dawson, S 2018, Understand students’ self-reflections through learning analytics. in SB Shum, R Ferguson, A Merceron & X Ochoa (eds), Proceedings of the 8th International Conference on Learning Analytics & Knowledge (LAK'18): Towards User-Centred Learning Analytics. Association for Computing Machinery (ACM), New York NY USA, pp. 389-398, International Learning Analytics & Knowledge Conference 2018, Sydney, New South Wales, Australia, 5/03/18. https://doi.org/10.1145/3170358.3170374

Understand students’ self-reflections through learning analytics. / Kovanović, Vitomir; Joksimović, Srećko; Mirriahi, Negin et al.

Proceedings of the 8th International Conference on Learning Analytics & Knowledge (LAK'18): Towards User-Centred Learning Analytics. ed. / Simon Buckingham Shum; Rebecca Ferguson; Agathe Merceron; Xavier Ochoa. New York NY USA : Association for Computing Machinery (ACM), 2018. p. 389-398.

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

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AU - Kovanović, Vitomir

AU - Joksimović, Srećko

AU - Mirriahi, Negin

AU - Blaine, Ellen

AU - Gašević, Dragan

AU - Siemens, George

AU - Dawson, Shane

N1 - Conference code: 8th

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N2 - Reflective writing has been widely recognized as one of the most effective activities for fostering students’ reflective and critical thinking. The analysis of students’ reflective writings has been the focus of many research studies. However, to date this has been typically a very labor-intensive manual process involving content analysis of student writings. With recent advancements in the field of learning analytics, there have been several attempts to use text analytics to examine student reflective writings. This paper presents the results of a study examining the use of theoretically-sound linguistic indicators of different psychological processes for the development of an analytics system for assessment of reflective writing. More precisely, we developed a random-forest classification system using linguistic indicators provided by the LIWC and Coh-Metrix tools. We also examined what particular indicators are representative of the different types of student reflective writings.

AB - Reflective writing has been widely recognized as one of the most effective activities for fostering students’ reflective and critical thinking. The analysis of students’ reflective writings has been the focus of many research studies. However, to date this has been typically a very labor-intensive manual process involving content analysis of student writings. With recent advancements in the field of learning analytics, there have been several attempts to use text analytics to examine student reflective writings. This paper presents the results of a study examining the use of theoretically-sound linguistic indicators of different psychological processes for the development of an analytics system for assessment of reflective writing. More precisely, we developed a random-forest classification system using linguistic indicators provided by the LIWC and Coh-Metrix tools. We also examined what particular indicators are representative of the different types of student reflective writings.

KW - Learning analytics

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BT - Proceedings of the 8th International Conference on Learning Analytics & Knowledge (LAK'18)

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ER -

Kovanović V, Joksimović S, Mirriahi N, Blaine E, Gašević D, Siemens G et al. Understand students’ self-reflections through learning analytics. In Shum SB, Ferguson R, Merceron A, Ochoa X, editors, Proceedings of the 8th International Conference on Learning Analytics & Knowledge (LAK'18): Towards User-Centred Learning Analytics. New York NY USA: Association for Computing Machinery (ACM). 2018. p. 389-398 https://doi.org/10.1145/3170358.3170374

Understand students’ self-reflections through learning analytics

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Keywords

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