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.

Solving mathematics story problems requires text comprehension skills. However, previous studies have found few connections between traditional measures of text readability and performance on story problems. We hypothesized that recently developed measures of readability and topic incidence measured by text-mining tools may illuminate associations between text difficulty and problem-solving measures. We used data from 3,216 middle and high school students from 10 schools using the Cognitive Tutor Algebra program; these schools were geographically, socioeconomically, racially, and ethnically diverse. We found that several indicators of the readability and topic of story problems were associated with students' tendency to give correct answers and request hints in Cognitive Tutor. We further examined the individual skill of writing an algebraic expression from a story scenario, and examined students at the lowest performing schools in the sample only, and found additional associations for these subsets. Key readability and topic categories that were related to problem-solving measures included word difficulty, text length, pronoun use, sentence similarity, and topic familiarity. These findings are discussed in the context of models of mathematics story problem solving and previous research on text comprehension.

Connection to students' individual interests helps imprint mathematics concepts.

Grounded and embodied cognition (GEC) serves as a framework to investigate mathematical reasoning for proof (reasoning that is logical, operative, and general), insight (gist), and intuition (snap judgment). Geometry is the branch of mathematics concerned with generalizable properties of shape and space. Mathematics experts (N ϭ 46) and nonexperts (N ϭ 44) were asked to judge the truth and to justify their judgments for four geometry conjectures. Videotaped interviews were transcribed and coded for occurrences of gestures and speech during the proof production process. Analyses provide empirical support for claims that geometry proof production is an embodied activity, even when controlling for math expertise, language use, and spatial ability. Dynamic depictive gestures portray generalizable properties of shape and space through enactment of transformational operations (e.g., dilation, skewing). Occurrence of dynamic depictive gestures and nondynamic depictive gestures are associated with proof performance, insight, and intuition, as hypothesized, over and above contributions of spoken language. Geometry knowledge for proof may be embodied and accessed and revealed through actions and the transformational speech utterances describing these actions. These findings have implications for instruction, assessment of embodied knowledge, and the design of educational technology to facilitate mathematical reasoning by promoting and tracking dynamic gesture production and transformational speech. Educational Impact and Implications StatementHow do mathematical intuitions arise, and how can they help with advanced forms of reasoning such as geometry proofs? One idea is that intuitions arise from body movements that allow people to directly experience mathematical ideas and relationships. We analyzed videotaped interviews of 46 mathematics experts and 44 nonexperts and found they are each more likely to show correct mathematical intuitions and generate mathematically valid proofs when they produced gestures while speaking. The research findings contribute to theories of embodied cognition by showing that people can tap into nonverbal ways of mathematical thinking. This work is important for education in STEM (science, technology, engineering, and mathematics) because it demonstrates that embodied cognition applies beyond basic mathematics such as counting and computation to conceptual forms of reasoning involved in geometry proofs.