Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821951
Title: Analytics of time management strategies in online learning environments : a novel methodological approach
Author: Ahmad Uzir, Nora'Ayu Binti
ISNI:       0000 0005 0286 3843
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2020
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Abstract:
The emergence of technology-supported education, e.g., blended and online, has changed the global higher education landscape. Importantly, the new learning modes involve more complex tasks and challenging ways of learning that require effective time management and strong self-regulation skills. In this regard, one of the most prevalent theoretical lenses to understand learning processes is Self-Regulated Learning (SRL). In reference to SRL models, time is a major resource in learning. The way learners schedule, plan, and enact tactics and strategies on their learning time could tremendously impact their academic achievement. However, the assessment of how learners make time-related decisions in learning is a daunting task, particularly given its latent nature and inherent autonomous learning capacity. One way to address this problem is to make use of unprecedented volumes of data collected by digital learning environments that are precisely timestamped records of actions that learners take while studying. This thesis presents a set of novel learning analytics methods for detecting and understanding time management strategies based on the analysis of digital trace data collected in online learning environments. First, the thesis proposes a new method to detect time management tactics and strategies using a combination of sequence mining and clustering techniques. The thesis also describes how time management tactics and strategies detected with this method are aligned with an SRL model that is used as a theoretical foundation of this thesis. Second, the thesis introduces a novel learning analytics method for the detection of time management tactics and strategies. This method uses a combination of process mining and clustering techniques followed by a complementary process mining technique that has a unique feature to bring insights into the temporal learning processes. This new method also has a strong potential to inform and enhance understanding of how learners make complex decisions about their learning. Third, the thesis investigates mutual connections between time management and learning strategies and their combined connections with academic performance using epistemic network analysis. This analysis provides empirical evidence that supports the proposition that time management is a critical characteristic of effective self-regulated learners. Fourth, the thesis proposes a novel method that integrates computational and visualization techniques to explore the frequency, connections, ordering, and the time of the execution of time management and learning tactics, which usually been done in isolation in the existing literature. Then, the thesis quantitatively and theoretically compare time management and learning strategies detected with this new method to explore the role of time management and learning strategies in learning as drawing on theories of educational psychology. Fifth, this new method was validated in a study that was conducted on the trace data of different learning modalities and interaction modes, where large cohorts are involved. This final study emphasizes the importance of multivocality approach in the study of time management and other relevant learning constructs. Finally, the thesis concludes with a discussion of practical implications, the significance of the results, and future research directions.
Supervisor: Gasevic, Dragan ; Pardo, Abelardo ; Tsai, Yi-Shan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.821951  DOI:
Keywords: learning analytics ; self-regulated learning ; time management strategies ; learning strategies ; educational data mining
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