Introducing meaning to clicks

towards traced-measures of self-efficacy and cognitive load

Jelena Jovanović, Shane Dawson, Dragan Gašević, Alexander Whitelock-Wainwright, Abelardo Pardo

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

Abstract

The use of learning trace data together with various analytical methods has proven successful in detecting patterns in learning behaviour, identifying student profiles, and clustering learning resources. However, interpretation of the findings is often difficult and uncertain due to a lack of contextual data (e.g., data on student motivation, emotion or curriculum design). In this study we explored the integration of student self-reports about cognitive load and self-efficacy into the learning process and collection of relevant students' perceptions as learning traces. Our objective was to examine the association of traced measures of relevant learning constructs (cognitive load and self-efficacy) with i) indicators of the students' learning behaviour derived from trace data, and ii) the students' academic performance. The results indicated the presence of association between some indicators of students' engagement with learning activities and traced measures of cognitive load and self-efficacy. Correlational analysis demonstrated significant positive correlation between the students' course performance and traced measures of cognitive load and self-efficacy.

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
PublisherAssociation for Computing Machinery (ACM)
Pages511-520
Number of pages10
ISBN (Electronic)9781450362566
ISBN (Print)New York NY USA
DOIs
Publication statusPublished - 2019
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

  • Cognitive load
  • Learning analytics
  • Perceived difficulty
  • Self-efficacy
  • Self-reports
  • Trace data

Cite this

Jovanović, J., Dawson, S., Gašević, D., Whitelock-Wainwright, A., & Pardo, A. (2019). Introducing meaning to clicks: towards traced-measures of self-efficacy and cognitive load. 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. 511-520). (ACM International Conference Proceeding Series). Association for Computing Machinery (ACM). https://doi.org/10.1145/3303772.3303782
Jovanović, Jelena ; Dawson, Shane ; Gašević, Dragan ; Whitelock-Wainwright, Alexander ; Pardo, Abelardo. / Introducing meaning to clicks : towards traced-measures of self-efficacy and cognitive load. 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. Association for Computing Machinery (ACM), 2019. pp. 511-520 (ACM International Conference Proceeding Series).
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abstract = "The use of learning trace data together with various analytical methods has proven successful in detecting patterns in learning behaviour, identifying student profiles, and clustering learning resources. However, interpretation of the findings is often difficult and uncertain due to a lack of contextual data (e.g., data on student motivation, emotion or curriculum design). In this study we explored the integration of student self-reports about cognitive load and self-efficacy into the learning process and collection of relevant students' perceptions as learning traces. Our objective was to examine the association of traced measures of relevant learning constructs (cognitive load and self-efficacy) with i) indicators of the students' learning behaviour derived from trace data, and ii) the students' academic performance. The results indicated the presence of association between some indicators of students' engagement with learning activities and traced measures of cognitive load and self-efficacy. Correlational analysis demonstrated significant positive correlation between the students' course performance and traced measures of cognitive load and self-efficacy.",
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Jovanović, J, Dawson, S, Gašević, D, Whitelock-Wainwright, A & Pardo, A 2019, Introducing meaning to clicks: towards traced-measures of self-efficacy and cognitive load. 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), pp. 511-520, International Learning Analytics & Knowledge Conference 2019, Tempe, United States of America, 4/03/19. https://doi.org/10.1145/3303772.3303782

Introducing meaning to clicks : towards traced-measures of self-efficacy and cognitive load. / Jovanović, Jelena; Dawson, Shane; Gašević, Dragan; Whitelock-Wainwright, Alexander; 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. Association for Computing Machinery (ACM), 2019. p. 511-520 (ACM International Conference Proceeding Series).

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

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AU - Pardo, Abelardo

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Y1 - 2019

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AB - The use of learning trace data together with various analytical methods has proven successful in detecting patterns in learning behaviour, identifying student profiles, and clustering learning resources. However, interpretation of the findings is often difficult and uncertain due to a lack of contextual data (e.g., data on student motivation, emotion or curriculum design). In this study we explored the integration of student self-reports about cognitive load and self-efficacy into the learning process and collection of relevant students' perceptions as learning traces. Our objective was to examine the association of traced measures of relevant learning constructs (cognitive load and self-efficacy) with i) indicators of the students' learning behaviour derived from trace data, and ii) the students' academic performance. The results indicated the presence of association between some indicators of students' engagement with learning activities and traced measures of cognitive load and self-efficacy. Correlational analysis demonstrated significant positive correlation between the students' course performance and traced measures of cognitive load and self-efficacy.

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SN - New York NY USA

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Jovanović J, Dawson S, Gašević D, Whitelock-Wainwright A, Pardo A. Introducing meaning to clicks: towards traced-measures of self-efficacy and cognitive load. 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. Association for Computing Machinery (ACM). 2019. p. 511-520. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3303772.3303782