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Context personalization-the incorporation of students' out-of-school interests into learning tasks-has recently been shown to positively affect students' situational interest and their performance and learning in mathematics. However, few studies have shown effects on both interest and achievement, drawing into question whether context personalization interventions can achieve both ends. The effects of personalization are theorized to result from activation of students' prior knowledge of personal interests and generation of situational interest in math tasks, though theorists have begun to question whether situational interest serves as a mechanism by which learning outcomes are achieved. This experimental study examines whether personalizing 4 units of algebra problems that high school students (N ϭ 150) solve in an intelligent tutoring system could improve their performance in units (i.e., accuracy and learning efficiency) and on classroom exams, whether adolescents who solved personalized problems would report greater situational interest in units (and later, individual interest in math) than peers who solved standard problems, and whether paths through situational interest would contribute to effects of personalization on outcomes. High school students in the personalization condition reported greater triggered situational interest in experimental units, and triggered interest predicted in-tutor outcomes (accuracy, learning efficiency). A total effect of personalization was also observed on classroom exam performance and individual interest in mathematics. Implications for theories of interest and context personalization are discussed, as are implications for math instruction and design of personalized learning environments. Educational Impact and Implications StatementContext personalization refers to an instructional design strategy that incorporates students' out-of-school interests into learning tasks like math problems. Recent research has shown that personalization positively affects students' situational interest and their performance and learning in math, but students seldom obtain both outcomes. This study confirmed that personalizing 4 units of algebra story problems to students' out-of-school interests was sufficient to increase their situational interest in the task and to improve the efficiency with which they solved problems within the intelligent tutoring system. Months later, those who solved personalized problems also reported greater interest in mathematics and scored higher on a classroom math test than a control group. These results extend evidence for the benefits of personalization and confirm that personalizing problems to incorporate student interests at an appropriate depth and specificity can simultaneously produce effects on math interest and learning.

Self-regulated learning (SRL) theorists propose that learners' motivations and cognitive and metacognitive processes interact dynamically during learning, yet researchers typically measure motivational constructs as stable factors. In this study, self-efficacy was assessed frequently to observe its variability during learning and how learners' efficacy related to their problem-solving performance and behavior. Students responded to self-efficacy prompts after every fourth problem of an algebra unit completed in an intelligent tutoring system. The software logged students' problem-solving behaviors and performance. The results of stability and change, path, and correlational analyses indicate that learners' feelings of efficacy varied reliably over the learning task. Their prior performance (i.e., accuracy) predicted subsequent self-efficacy judgments, but this relationship diminished over time as judgments were decreasingly informed by accuracy and increasingly informed by fluency. Controlling for prior achievement and self-efficacy, increases in efficacy during one problemsolving period predicted help-seeking behavior, performance, and learning in the next period. Findings suggest that self-efficacy varies during learning, that students consider multiple aspects of performance to inform their efficacy judgments, and that changes in efficacy influence self-regulated learning processes and outcomes.

Students who drop out of their science, technology, engineering, and math (STEM) majors commonly report that they lack skills critical to STEM learning and career pursuits. Many training programs exist to develop students’ learning skills and they typically achieve small to medium effects on behaviors and performance. However, these programs require large investments of students’ and instructors’ time and effort, which limits their applicability to large lecture course formats commonly employed in early undergraduate STEM coursework. This study examined whether brief, digital training modules designed to help students apply learning strategies and self-regulated learning principles effectively in their STEM courses can impact students’ behaviors and performance in a large biology lecture course. Results indicate that a 2-hr Science of Learning to Learn training had significant effects on students’ use of resources for planning, monitoring, and strategy use, and improved scores on quizzes and exams. These findings indicate that a brief, self-guided, online training can increase desirable learning behaviors and improve STEM performance with minimal cost to learners or instructors. Implications for future design of interventions and their provision to students in need of support are discussed.