The paper describes a theoretical framework for the study of teachers’ promotion of self-regulated learning in the classroom. The Self-Regulated Learning Teacher Promotion Framework (SRL-TPF) utilizes the ICAP theory to assess the affordances of the learning environment for the indirect promotion of SRL, proposes new variables in the investigation of the direct promotion of SRL, and examines how these two ways to promote SRL are related. The SRL-TPF was used to examine the direct and indirect promotion of SRL in filmed observations of 23 Australian classrooms. The results revealed a paucity in the design of Constructive and Interactive lesson tasks that support the indirect promotion of SRL and a preference for the direct support of SRL through implicit strategy instruction and the provision of metacognitive reflection and support. There were important teacher differences in both the direct and indirect promotion of SRL, but the teachers who were more likely to design Constructive and Interactive lesson tasks did not necessarily promote SRL directly and vice versa. The research contributes to a better understanding of the relationship between teaching what to learn (subject content) and how to learn (SRL knowledge and strategies).

Self-regulated learning (SRL) is crucial to students’ learning. SRL is characterized by students taking initiative, showing perseverance and adaptively regulating their learning. Teachers play an essential role in promoting and fostering this process. However, several studies have shown that in primary education explicit instruction of SRL strategies barely takes place. Given the relevance of SRL for learning and preparing students for the knowledge society of the 21st century, it is of crucial importance that teachers in primary education learn how they can improve their students’ SRL. In the present study, we implemented a professional development program (iSELF) in which primary teachers were trained and coached in promoting and fostering their students’ SRL. The extent to which iSELF contributed to teachers’ explicit instruction of SRL strategies was evaluated in a quasi-experimental pre-test-post-test design using video-based classroom observations. Thirty teachers from fourteen different primary schools participated in this study and were assigned to either a control (twelve teachers) or an experimental group (eighteen teachers). Results indicate that in both conditions explicit SRL strategy instruction is rare. However, explicit instruction of SRL strategies is significantly higher in the experimental group on the post-test compared to the control group showing that teachers do benefit from learning about explicit SRL instruction.

It is important for learners to engage in self-regulated learning (SRL), as it predicts academic achievement in a wide range of disciplines. However, SRL can be difficult to enact. Therefore, scaffolds have been designed to support SRL. In our introductory article to this special issue on facilitating SRL with scaffolds, we present a framework to categorize different scaffolds, place the contributions to this special issue in the framework, present highlights from the contributions, and conclude with a discussion on designing scaffolds to facilitate SRL.

Students often make incorrect predictions about their exam performance, with the lowest-performing students showing the greatest inaccuracies in their predictions. The reasons why low-performing students make inaccurate predictions are not fully understood. In two studies, we tested the hypothesis that low-performing students erroneously predict their exam performance in part because their past performance varies considerably, yielding unreliable data from which to make their predictions. In contrast, high-performing students tend to have consistently high past performance that they can rely on to make relatively accurate predictions of future test performance. Results showed that across different exams (Study 1) and different courses (Study 2), low-performing students had more variable past performance than high-performing students. Further, results from Study 2 showed that variability in past course performance (but not past exam performance) was associated with poor calibration. Results suggest that variability in past performance may be one factor that contributes to low-performing students’ erroneous performance predictions.