3.8 Article

Contextualized Logging of On-Task and Off-Task Behaviours During Learning

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JOURNAL OF LEARNING ANALYTICS
卷 10, 期 2, 页码 115-125

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SOC LEARNING ANALYTICS RESEARCH-SOLAR
DOI: 10.18608/jla.2023.7837

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Learners use digital media for various reasons during learning, either on-task or off-task. Logging the activities on digital devices can explore the usage of digital media in learning scenarios, but it faces challenges like logging only during learning, invasion of privacy, incorporating multiple devices, and revealing the reasons for switching activities. To address these issues, a contextually activated logging system is proposed, which allows users to inspect and annotate the observed activities after the learning session. A formative study shows that the system works as intended and supports the assumptions about the diverse intentions of media use in learning.
Learners use digital media during learning for a variety of reasons. Sometimes media use can be considered on -task, e.g., to perform research or to collaborate with peers. In other cases, media use is off-task, meaning that learners use content unrelated to their current learning task. Given the well-known problems with self-reported data (incomplete memory, distorted perceptions, subjective attributions), exploring on-task and off-task usage of digital media in learning scenarios requires logging activity on digital devices. However, we argue that logging on-and off -task behaviour has challenges that are rarely addressed. First, logging must be active only during learning. Second, logging represents a potential invasion of privacy. Third, logging must incorporate multiple devices simultaneously to take the reality of media multitasking into account. Fourth, logging alone is insufficient to reveal what prompted learners to switch to a different digital activity. To address these issues, we present a contextually activated logging system that allows users to inspect and annotate the observed activities after a learning session. Data from a formative study show that our system works as intended, and furthermore supports our assumptions about the diverse intentions of media use in learning. We discuss the implications for learning analytics.

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