Analytics of learning strategies

associations with academic performance and feedback

Wannisa Matcha, Dragan Gašević, Nora'Ayu Ahmad Uzir, Jelena Jovanović, Abelardo Pardo

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

Learning analytics has the potential to detect and explain characteristics of learning strategies through analysis of trace data and communicate the findings via feedback. However, the role of learning analytics-based feedback in selection and regulation of learning strategies is still insufficiently explored and understood. This research aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feedback. Three years of trace data were collected from online pre-class activities of a flipped classroom, where different types of feedback were employed in each year. Clustering, sequence mining, and process mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the association of feedback with the learning performance and the detected learning strategies. The results suggest a positive association between the personalised feedback and the effective strategies.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19)
Subtitle of host publicationLearning Analytics to Promote Inclusion and Success
EditorsChristopher Brooks, Rebecca Ferguson, Ulrich Hoppe
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages461-470
Number of pages10
ISBN (Electronic)9781450362566
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventInternational Learning Analytics & Knowledge Conference 2019 - Arizona State University, Tempe, United States of America
Duration: 4 Mar 20198 Mar 2019
Conference number: 9th
https://lak19.solaresearch.org/

Publication series

NameACM International Conference Proceeding Series

Conference

ConferenceInternational Learning Analytics & Knowledge Conference 2019
Abbreviated titleLAK 2019
CountryUnited States of America
CityTempe
Period4/03/198/03/19
Internet address

Keywords

  • Data mining
  • Feedback
  • Learning analytics
  • Learning strategies
  • Learning tactics
  • Self-regulated learning

Cite this

Matcha, W., Gašević, D., Uzir, NA. A., Jovanović, J., & Pardo, A. (2019). Analytics of learning strategies: associations with academic performance and feedback. In C. Brooks, R. Ferguson, & U. Hoppe (Eds.), Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19): Learning Analytics to Promote Inclusion and Success (pp. 461-470). (ACM International Conference Proceeding Series). New York NY USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3303772.3303787
Matcha, Wannisa ; Gašević, Dragan ; Uzir, Nora'Ayu Ahmad ; Jovanović, Jelena ; Pardo, Abelardo. / Analytics of learning strategies : associations with academic performance and feedback. Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19): Learning Analytics to Promote Inclusion and Success. editor / Christopher Brooks ; Rebecca Ferguson ; Ulrich Hoppe. New York NY USA : Association for Computing Machinery (ACM), 2019. pp. 461-470 (ACM International Conference Proceeding Series).
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title = "Analytics of learning strategies: associations with academic performance and feedback",
abstract = "Learning analytics has the potential to detect and explain characteristics of learning strategies through analysis of trace data and communicate the findings via feedback. However, the role of learning analytics-based feedback in selection and regulation of learning strategies is still insufficiently explored and understood. This research aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feedback. Three years of trace data were collected from online pre-class activities of a flipped classroom, where different types of feedback were employed in each year. Clustering, sequence mining, and process mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the association of feedback with the learning performance and the detected learning strategies. The results suggest a positive association between the personalised feedback and the effective strategies.",
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Matcha, W, Gašević, D, Uzir, NAA, Jovanović, J & Pardo, A 2019, Analytics of learning strategies: associations with academic performance and feedback. in C Brooks, R Ferguson & U Hoppe (eds), Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19): Learning Analytics to Promote Inclusion and Success. ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), New York NY USA, pp. 461-470, International Learning Analytics & Knowledge Conference 2019, Tempe, United States of America, 4/03/19. https://doi.org/10.1145/3303772.3303787

Analytics of learning strategies : associations with academic performance and feedback. / Matcha, Wannisa; Gašević, Dragan; Uzir, Nora'Ayu Ahmad; Jovanović, Jelena; Pardo, Abelardo.

Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19): Learning Analytics to Promote Inclusion and Success. ed. / Christopher Brooks; Rebecca Ferguson; Ulrich Hoppe. New York NY USA : Association for Computing Machinery (ACM), 2019. p. 461-470 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

TY - GEN

T1 - Analytics of learning strategies

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AU - Jovanović, Jelena

AU - Pardo, Abelardo

PY - 2019

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AB - Learning analytics has the potential to detect and explain characteristics of learning strategies through analysis of trace data and communicate the findings via feedback. However, the role of learning analytics-based feedback in selection and regulation of learning strategies is still insufficiently explored and understood. This research aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feedback. Three years of trace data were collected from online pre-class activities of a flipped classroom, where different types of feedback were employed in each year. Clustering, sequence mining, and process mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the association of feedback with the learning performance and the detected learning strategies. The results suggest a positive association between the personalised feedback and the effective strategies.

KW - Data mining

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BT - Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19)

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Matcha W, Gašević D, Uzir NAA, Jovanović J, Pardo A. Analytics of learning strategies: associations with academic performance and feedback. In Brooks C, Ferguson R, Hoppe U, editors, Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19): Learning Analytics to Promote Inclusion and Success. New York NY USA: Association for Computing Machinery (ACM). 2019. p. 461-470. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3303772.3303787