Abstract
Four high school students received 11 weeks of a self-regulated learning (SRL) intervention, called the Self-Regulation Empowerment Program (SREP), to improve their classroom-based biology exam scores, SRL, and motivated behaviors. This mixed model case study examined the correspondence between shifts in students’ strategic, regulated behaviors with their performance on classroom-based biology tests. The authors used traditional SRL assessment tools in a pretest-posttest fashion (e.g., self-report questionnaires, teaching rating scales) and gathered SRL data during the intervention using field note observations and contextualized structured interviews. This multidimensional assessment approach was used to establish convergence among the assessment tools and to facilitate interpretation of trends in students’ biology test performance relative to their SRL processes. Key themes in this study included the following: (a) the close correspondence between changes in students SRL, biology exam performance, and SREP attendance; (b) individual variability in student performance, SRL behaviors, and beliefs in response to SREP; and (c) the importance of using a multi-dimensional assessment approach in SRL intervention research. Furthermore, this study provided additional support for the potential effectiveness of SREP in academic contexts.
Highlights
E importance and positive impact of self-regulation learning (SRL) processes on the academic achievement of students have been consistently demonstrated over the past couple of decades across a multitude of research methodologies and contexts [1,2,3,4,5,6]
Of particular relevance to educators, is research evaluating the impact of SRL intervention programs on the basic academic skills, such as reading, math, and writing [2, 3, 5]. is line of research is important because it underscores the potential utility of infusing SRL principles into academic intervention programs or authentic classroom instructional contexts
Given that these activities o en occur outside the supervision of teachers and necessitate the use of efficient regulatory strategies and processes, attention devoted to how students regulate and use such strategies as they prepare for exams is critical. e primary focus of this paper is to investigate, through four case studies, the correspondence between shi s in high school students’ strategic and regulated behaviors during test preparation and their overall test performance and attendance to an SRL intervention program, called the Self-Regulation Empowerment Program
Summary
E importance and positive impact of self-regulation learning (SRL) processes on the academic achievement of students have been consistently demonstrated over the past couple of decades across a multitude of research methodologies and contexts [1,2,3,4,5,6]. In a series of recent surveys, for example, teachers and school psychologists identi ed SRL as a critical determinant of student success [7,8,9] These school-based personnel expressed poor personal knowledge in motivation and SRL processes, a lack of experience incorporating these principles into their teaching or professional activities, and a strong desire to receive professional development training regarding implementing SRL assessment tools and interventions. Students are o en faced with many challenges or demands as they prepare for exams, most notably the need to organize and integrate large volumes of information presented within class lessons, homework assignments, and/or readings from relevant texts and resources [22] Given that these activities o en occur outside the supervision of teachers and necessitate the use of efficient regulatory strategies and processes, attention devoted to how students regulate and use such strategies as they prepare for exams is critical. Zimmerman [25] explicitly de nes self-regulation as a process operationalized in terms of a contextualized, cyclical feedback loop. at is, individuals proactively plan and initiate learning attempts and use self-generated or externally provided feedback to modify and adapt their learning methods to optimize performance
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