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

Abstract

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...

Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018)

“…From the review, it can be noted that measurement and promotion of SRL has greatly advanced from the use of traditional methods which relied on learner perceptions on their SRL skills and use of offline data to online measures such as the use of learner logs from e-learning environments. The findings from the review indicate that there is noticeable evidence of a recognizable shift from using tools that only measures SRL to tools that measure SRL while providing interventions that stimulates growth of SRL in learners during the learning process Barnard et al, 2009;Cho & Shen, 2013;Dawson et al, 2015;Delen et al, 2014;Onah & Sinclair, 2017;Siadaty, 2016;Winne & Hadwin, 2013).…”

“…When the learners engage on the suggested SRL tasks by agents, the SRL skill growth is triggered. The software agents identified in this review include ProSOLO software by Dawson et al (2015), eLDa tool by Onah and Sinclair (2017), SoftLearn tool by Rodriguez Groba et al (2014), and Learn-B software by Siadaty (2016). c. Learner feedback: As presented in Table 2, we established that a number of studies employed web-enabled prompts to provide feedback to learners.…”

“…The tools are also deployed outside the learning environment before or after a learning episode and therefore not able to measure SRL during an actual learning episode when skills are being employed by students. (Broadbent & Poon, 2015;Lee, 2008;Roth et al, 2016;Saks & Leijen, 2014;Siadaty, 2016;Winne & Perry, 2000) The traditional tools and methods are obtrusive…”

“…The learners are normally aware of SRL being measured and therefore affect their engagement and performance (Schraw, 2010) (Siadaty, 2016) Existence of many models and many constructs to be measured…”

Unobtrusive Observation of Team Learning Attributes in Digital Learning

“…In the case of assessments, the new opportunities for analytics can be extended to include legitimate peripheral participation (Lave and Wenger, 1991) in team learning by documenting individual behaviors, actions, and problem-solving strategies while working in the social setting of team work (Kinshuk Ifenthaler et al, 2010). Digital games and simulations designed for team performance for example, often are characterized by integrated, media-rich contexts with multiple layers of interaction with peers as well as computational resources, which provides a foundation for authentic performance of individual and team-based problem-solving processes (Clarke-Midura et al, 2012) with attendant opportunities for unobtrusive observation and documentation of strategies, tools, communications, intentional actions and artifacts (Griffin et al, 2012; Siadaty et al, 2015). …”