Article Preview
TopIntroduction
Accompanying the rapid expansion of blended and online learning in professional and educational environments, researchers claim two factors adversely impact formal and informal learning processes and typically lower performance: (1) lack of academic motivation, and (2) poor time management for task completion (Berestova et al., 2022). Looking specifically at self-directed e-learning (SDEL) for job training, two effects - reduced social interactions and lower levels of engagement - generate higher dropout rates (Kim et al., 2012; Muilenburg & deBerge, 2007; de Freitas et al., 2015; Macfadyen & Dawson, 2010). To meet performance goals, adults who engage with online learning should monitor and control their learning processes by creating, monitoring, and adjusting their learning behaviors (Dabbagh & Kitsantas, 2004; Roll & Winne, 2015). Self-regulated learning (SRL) strategies can assist learners to check their progress toward a goal and manage their learning to achieve higher performance (Broadbent & Poon, 2015). Over the past decade, researchers have relied on data from self-report surveys to identify students with low SRL skills (Broadbent & Poon, 2015; Winne, 2010), while acknowledging limitations such as inaccuracy, subjectivity (Brown, 2017; Miller, 2016) and bias (Ganda & Boruchovitch, 2018).
Over the past 20 years, limited scholarship has synthesized how adults select SRL strategies to monitor and enhance their online performance or how SRL influences their learning experience in the context of self-directed e-learning (SDEL) (Kim & Frick, 2011; Kim et al., 2019). More recently, a few scholars have explored SRL strategies that a user may apply to promote their academic performance in computer-based learning environments (Leggett et al., 2013; Zheng, 2016). For example, some instructors deliberately modified a course design to assist students to manage their time more effectively. Others allowed students to set their own deadlines and suggested that students schedule their study time. Yet others encouraged students to plan a specific process to efficiently complete activities (Baker et al., 2019; Baker et al., 2016; Sitzmann and Johnson, 2012). In all, inconsistent findings suggest that individual circumstances beyond the learning environment may influence whether facilitating time management skills enhances performance across an entire course (Ariely & Wertenbroch, 2002; Burger et al., 2011; Levy & Ramim, 2013). Conflicting recommendations from these studies may be attributed to biases from self-reported data or contextual variations across programs and students.
In response to these constraints, we selected an innovative methodology to ascertain how individuals apply self-regulated learning strategies for improved task performance in a self-directed e-learning context. Clickstream data are information collected about a user that can be applied to observe, measure, and analyze recorded and real-time learning behaviors in a learning management system (LMS). Linking click-stream data with performance outcomes means researchers can create a more comprehensive assessment of learning behaviors and determine how this may impact academic performance in an online learning experience. We asked the following research question: Are students who apply SRL strategies more likely to demonstrate mastery in knowledge and skills in a self-directed e-Learning context?