Supporting Student Self-Regulation: Designing and Deploying Effective Microsurveys and Warning Systems to Help Learners Help Themselves

Introduction

Despite decades of research on student outcomes, students continue to withdraw, fail, and dropout of higher education (in particular at two-year and other broad access institutions) at an alarming rate. According to the National Student Clearinghouse Research Center, of those students who started college in the fall of 2016, 26% of students did not persist at any institution the following year, and 38% were not retained by their initial institution. At two-year institutions, only 48% were retained (NSC Research Center, 2018). Research has shown that when students feel less motivated to attend a class or do not see the relevance of that class to their degree goals, they visit the learning environment less frequently (Simpson, 2013). Courses taken online have a 10-20% higher drop rate than face-to-face courses, with 40-80% of online students dropping depending upon the institution and course (Bawa, 2016). Therefore, online courses and degree programs need systems that help to identify students who are at risk of dropping in time for instructors, advisors, and administrators to attempt to intervene. Traditionally, the main source of this data was end-of-course surveys, which by their very nature were too late (Hu, Lo & Shih, 2014). Today, early warning systems provide the opportunity to identify students early enough to provide an intervention.

Some of the best early warning systems and their associated interventions are those that help learners to better self-regulate. Self-regulation involves planning, monitoring, and improving one’s learning using a variety of strategies. Interventions that enable better self-regulation can not only help learners with the experience at hand, they can also help learners become better at learning in general. Self-regulation doesn’t just apply to learning; for example, work has been done to support self-regulation in behavioral and health contexts, including one scale that was designed to capture self-regulation associated with health risk behaviors (Scherer et al., 2022). Self-regulation refers to the ability to enlist one’s affective, behavioral, and cognitive resources to approach and achieve goals. The centrality of self-regulation to all goal-motivated behaviors, including learning, has prompted researchers to develop an ontology of self-regulation that can benefit microsurveys and associated interventions (Eisenberg et al., 2018).

According to the research, SRL strategies overwhelmingly improve learning performance (Dignath & Büttner 2008; Dignath et al., 2008; Hattie et al., 1996; Richardson et al., 2012; Sitzmann & Ely, 2011; Theobald, 2021). Researchers conducted a systematic review of 14 papers, qualified from an initial set of more than 239 studies, and determined that SRL strategies positively correlate with non-academic outcomes (Anthonysamy et al., 2020). However, SRL is most often associated with academic outcomes. For example, researchers found that students' learning outcomes were influenced by their self-control, SRL, and course participation. The impact of self-control on learning outcomes was mediated through SRL and course participation; specifically, self-control and SRL predicted final grades in the course (Zhu et al., 2016).