Analytics of time management strategies in a flipped classroom

Nora’ayu Ahmad Uzir; Dragan Gašević; Wannisa Matcha; Jelena Jovanovic; Abelardo Pardo

Analytics of time management strategies in a flipped classroom

Nora’ayu Ahmad Uzir, Dragan Gašević, Wannisa Matcha, Jelena Jovanovic, Abelardo Pardo

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

Abstract

This study aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. The study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N=1, 134). Trace data about activities were initially coded for the timeliness of activity completion. Such data were then analyzed using agglomerative hierarchical clustering based on the Ward's algorithm, firsst order Markov chains, and inferential statistics to detect time management tactics and strategies from students' learning activities. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace daata as manifestations of students' tactics and strategies. In addition, this study also showed that time management tactics had significant associations with academic performance.

Original language	English
Title of host publication	Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19)
Editors	Christopher Brooks, Rebecca Ferguson, Ulrich Hoppe
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	235-237
Number of pages	2
Publication status	Published - 2019
Event	International Learning Analytics & Knowledge Conference 2019 - Arizona State University, Tempe, United States of America Duration: 4 Mar 2019 → 8 Mar 2019 Conference number: 9th https://lak19.solaresearch.org/

Conference

Conference	International Learning Analytics & Knowledge Conference 2019
Abbreviated title	LAK 2019
Country	United States of America
City	Tempe
Period	4/03/19 → 8/03/19
Internet address	https://lak19.solaresearch.org/

Keywords

learning analytics
time management
flipped learning
self-regulated learning

Access to Document

298866202-oaFinal published version, 123 KB

https://www.solaresearch.org/core/companion-proceedings-of-the-9th-international-learning-analytics-and-knowledge-conference-lak19/

Cite this

Ahmad Uzir, N., Gašević, D., Matcha, W., Jovanovic, J., & Pardo, A. (2019). Analytics of time management strategies in a flipped classroom. In C. Brooks, R. Ferguson, & U. Hoppe (Eds.), Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19) (pp. 235-237). Association for Computing Machinery (ACM). https://www.solaresearch.org/core/companion-proceedings-of-the-9th-international-learning-analytics-and-knowledge-conference-lak19/

Ahmad Uzir, Nora’ayu ; Gašević, Dragan ; Matcha, Wannisa ; Jovanovic, Jelena ; Pardo, Abelardo. / Analytics of time management strategies in a flipped classroom. Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19). editor / Christopher Brooks ; Rebecca Ferguson ; Ulrich Hoppe. New York NY USA : Association for Computing Machinery (ACM), 2019. pp. 235-237

@inproceedings{0ec6643897fa4852955bc040709e41af,

title = "Analytics of time management strategies in a flipped classroom",

abstract = "This study aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. The study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N=1, 134). Trace data about activities were initially coded for the timeliness of activity completion. Such data were then analyzed using agglomerative hierarchical clustering based on the Ward's algorithm, firsst order Markov chains, and inferential statistics to detect time management tactics and strategies from students' learning activities. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace daata as manifestations of students' tactics and strategies. In addition, this study also showed that time management tactics had significant associations with academic performance. ",

keywords = "learning analytics, time management, flipped learning, self-regulated learning",

author = "{Ahmad Uzir}, Nora{\textquoteright}ayu and Dragan Ga{\v s}evi{\'c} and Wannisa Matcha and Jelena Jovanovic and Abelardo Pardo",

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pages = "235--237",

editor = "Brooks, {Christopher } and Ferguson, {Rebecca } and Hoppe, {Ulrich }",

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publisher = "Association for Computing Machinery (ACM)",

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Ahmad Uzir, N, Gašević, D, Matcha, W, Jovanovic, J & Pardo, A 2019, Analytics of time management strategies in a flipped classroom. in C Brooks, R Ferguson & U Hoppe (eds), Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19). Association for Computing Machinery (ACM), New York NY USA, pp. 235-237, International Learning Analytics & Knowledge Conference 2019, Tempe, United States of America, 4/03/19. <https://www.solaresearch.org/core/companion-proceedings-of-the-9th-international-learning-analytics-and-knowledge-conference-lak19/>

Analytics of time management strategies in a flipped classroom. / Ahmad Uzir, Nora’ayu; Gašević, Dragan; Matcha, Wannisa; Jovanovic, Jelena; Pardo, Abelardo.

Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19). ed. / Christopher Brooks; Rebecca Ferguson; Ulrich Hoppe. New York NY USA : Association for Computing Machinery (ACM), 2019. p. 235-237.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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T1 - Analytics of time management strategies in a flipped classroom

AU - Ahmad Uzir, Nora’ayu

AU - Gašević, Dragan

AU - Matcha, Wannisa

AU - Jovanovic, Jelena

AU - Pardo, Abelardo

N1 - Conference code: 9th

PY - 2019

Y1 - 2019

N2 - This study aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. The study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N=1, 134). Trace data about activities were initially coded for the timeliness of activity completion. Such data were then analyzed using agglomerative hierarchical clustering based on the Ward's algorithm, firsst order Markov chains, and inferential statistics to detect time management tactics and strategies from students' learning activities. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace daata as manifestations of students' tactics and strategies. In addition, this study also showed that time management tactics had significant associations with academic performance.

AB - This study aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. The study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N=1, 134). Trace data about activities were initially coded for the timeliness of activity completion. Such data were then analyzed using agglomerative hierarchical clustering based on the Ward's algorithm, firsst order Markov chains, and inferential statistics to detect time management tactics and strategies from students' learning activities. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace daata as manifestations of students' tactics and strategies. In addition, this study also showed that time management tactics had significant associations with academic performance.

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