A Data Repository for the EDM Community: The PSLC DataShop

Koedinger, Kenneth R.; Baker, Ryan S.; Cunningham, Kyle; Skogsholm, Alida; Leber, Brett; Stamper, John C.

doi:10.1201/b10274-10

Cited by 48 publications

(13 citation statements)

References 15 publications

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“…To analyze these logs at the behavioral unit or grain size required by the educational research, we need to reformat the raw learning logs by systematically aggregating them and pass from low level events to high level learning actions [85]. Thus, it is necessary to define different abstractions of the log file data, such as:…”

Section: Data Filteringmentioning

confidence: 99%

A Survey on Pre-Processing Educational Data

Romero

Ventura

2013

Studies in Computational Intelligence

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Section: Data Filteringmentioning

confidence: 99%

A Survey on Pre-Processing Educational Data

Romero

Ventura

2013

Studies in Computational Intelligence

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“…Affordances of technology-enhanced learning environments provide opportunities to measure SRL processes based on learners' traces (Greene & Azevedo, 2010;Winne, 2010b;Zhou et al, 2010). Because traces are gathered within the learning environment and in their authentic context, they empower researchers to track learners' choices thoroughly and precisely, interactions with learning content, learning actions, and tactics applied on the fly.…”

Section: Self-regulated Learning: Measurementmentioning

confidence: 99%

“…Because traces are gathered within the learning environment and in their authentic context, they empower researchers to track learners' choices thoroughly and precisely, interactions with learning content, learning actions, and tactics applied on the fly. This method is often referred to as Trace Methodology in the contemporary research on measuring SRL features (Nesbit, Xu, Zhou, & Winne, 2007;Winne & Perry, 2000;Zhou et al, 2010). Winne (Winne & Perry, 2000;Winne, 2010b) defines traces as "records of behaviour, a form of performance assessment, that provide grounds for inferring a learner's cognitive and metacognitive activities."…”

Section: Self-regulated Learning: Measurementmentioning

confidence: 99%

“…In general, traces can be any type of data collected from users' actions in a learning environment, such as their clicks on hyperlinks or options selected from a palette. Reading times and eye-tracking strategies are other examples of online, unobtrusive methods (Greene & Azevedo, 2010;Nesbit et al, 2006;Zhou et al, 2010).…”

Section: Self-regulated Learning: Measurementmentioning

confidence: 99%

“…Via this methodology, learners' actions are captured on the fly and in the authentic context of occurrence. One of the greatest advantages of trace-based methodologies is that they allow for grounding analyses and inferences drawn from analyses on accurate and authentic data that proximally identify learning events in their very own context (Azevedo, Moos, Johnson, & Chauncey, 2010;Greene & Azevedo, 2010;Winne, 2010aWinne, , 2010bWinne & Perry, 2000;Zhou, Xu, Nesbit, & Winne, 2010).…”

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confidence: 99%

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Trace-based Micro-analytic Measurement of Self-Regulated Learning Processes

2016

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To keep pace with today's rapidly growing knowledge-driven society, productive selfregulation of one's learning processes are essential. We introduce and discuss a trace-based measurement protocol to measure the effects of scaffolding interventions on self-regulated learning (SRL) processes. It guides tracing of learners' actions in a learning environment on the fly and translates these data into indicators of engagement in SRL processes that reflect learners' use of scaffolding interventions and contingencies between those events. Graphs of users' learning actions in a learning environment are produced. Our trace-based protocol offers a new methodological approach to investigating SRL and new ways to examine factors that affect learners' use of self-regulatory processes in technology-enhanced learning environments. Our application of the protocol was described in a study about Learn-B, a learning environment for SRL in the workplace. The findings of the work presented in this paper indicate that future research can gain substantially by using learning analytics based on users' trace data and merging them with other quantitative and qualitative techniques for researching SRL beliefs and processes.Keywords: Self-regulated learning, micro-level process, trace-based methodologies, learning analytics, graph theory, learning technology INTRODUCTIONSelf-regulated learning (SRL) is acknowledged as an essential skill for lifelong learning in today's knowledge-driven society (Klug, Ogrin, & Keller, 2011). It is often characterized as a process in which learners take initiative to identify their learning goals; and choose and regulate their learning strategies, cognitive resources, motivation, and behaviour to optimize their learning outcomes (Boekaerts, 1997;Winne, 2010a;Zimmerman, 1990). Several research studies show that, in various learning contexts, learners often sub-optimally regulate their learning processes or simply have inadequate models of their learning process that leads them to misevaluating their learning (Bjork, Dunlosky, & Kornell, 2013;Margaryan, Milligan, & Littlejohn, 2009;Winne, 2005). 184To address this challenge, self-regulatory scaffolds and pedagogical affordances have recently become of interest to researchers as a means to support learners' engagement in SRL processes (see, for example, Dabbagh & Kitsantas, 2005;Hadwin, Oshige, Gress, & Winne, 2010;McLoughlin & Lee, 2007;Zhou & Winne, 2012). Although empirical research has shown that scaffolding can foster learners' engagement in some of the main elements of metacognition and SRL -such as self-observation, selfreflection, or goal-orientation (Greene & Azevedo, 2010) -no methodological framework has yet been developed that can measure the impact of individual scaffolds, embedded tools, or different elements of SRL in the context in which they are used. The context also plays an important role in the effect of a given scaffold. Current research has shown that SRL is highly context dependent and specific features of a learning environme...

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