Learning from Learning Kits: gStudy Traces of Students’ Self-Regulated Engagements with Computerized Content

Abstract

Researching self-regulated learning (SRL) as a process that evolves across multiple episodes of studying poses large methodological challenges. While self-report data provide useful information about learners' perceptions of learning, these data are not reliable indicators of studying tactics learners actually use while studying, especially when learners are young children. We argue that self-reports about SRL need to be augmented by fine-grained traces that are records of learners' actual activities as they study. We describe how gStudy software unobtrusively collects detailed trace data about learners' use of study tactics as they engage with content presented in learning kits-collections of documents (e.g., texts, graphics, video clips) and tasks (e.g., notes, concept maps) on which learners operate to study. We suggest that trace data can advance research about how learners select, monitor, assemble, rehearse, and translate information to learn it, and provide raw materials for mapping SRL and its effects. Examples from the Life Cycles Learning Kit that supports grade 1 students learning about the life cycles of humans and frogs are given.

Know thy student! Combining learning analytics and critical reflections to develop a targeted intervention for promoting self-regulated learning.

“…In recent years, much of the research on self-regulation utilizing online tools has supplemented data from learning management systems with other data collection methods, including computer traces, think-aloud protocols, diaries of studying, direct observation, and microanalyses (Bannert, Reimann, & Sonnenberg, 2013;Ferreira, Simão, & da Silva, 2014;Hadwin et al, 2007;Perry & Winne, 2006;Schmitz & Wiese, 2006), or has focused on courses offered primarily or entirely online (Lynch & Dembo, 2004). However, in the non-research, on-campus environment when these additional tools are not available, what can the data that is available tell us?…”

“…Selfreporting is particularly useful for providing insight into such processes, which are not always easily tracked online in adult learners with clear preferences for certain self-regulatory strategies (for example, "Watching YouTube videos for concepts that I donʼt understand and need to clarify" and "Studying with friends in group discussions where we talk about concepts, and take turns explaining things to each other, in order to come to a group consensus about more difficult topics"). However, self-reported data has well-documented flaws, including variations in the interpretation of the learning context in question, and inaccuracies in self-reporting the frequency of strategies (Perry & Winne, 2006). The combined analysis of students' online access of data together with self-reporting within meta-learning assessment tasks allows a more comprehensive view of student self-regulatory behaviour.…”

Personalisation of Learning in Virtual Learning Environments

“…Some authors expressed interest for the exploitation of different kinds of interaction footprints by researchers [48,49]. Others speculated about its benefit for instructors [50].…”

Self-Regulation and Feedback in an Educational Statistics Course

“…projects, portfolios, presentations), have also been utilized to assess student learning (Nicol & Milligan, 2006). More recently, assessment content has been delivered via computer programs and through websites (Aleven, McLaren, Roll, & Koedinger, 2010;Azevedo, 2009;Biswas, Leelawong, Schwartz, Vye, & Teachable Agents Group, 2005;Greene, Moos, Azevedo, & Winters, 2008;Perry & Winne, 2006;Winne & Nesbit, 2009). Nicol and Milligan (2006) contend that although computer-based assessment differs from traditional paper-pencil classwork and homework, technology-supported assessment can also fit the criteria of good feedback practice, and in turn help students develop necessary self-regulation skills.…”