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From the socio-critical perspective of mathematical modeling, reflexive discussions about the nature, criteria, and consequences of mathematical models are not a natural consequence of modeling in school. This report is part of a larger study focused on stimulating reflexive discussions in practice employing constructivist grounded theory as a research method. Twenty-seven college algebra students engaged in a 3-week modeling project at a community college in the USA. Audio-recorded group discussions and written reflections were collected to determine how reflexive discussions were taking place. Analysis of students' actions and reflexive discussions during the modeling project produced four concepts: "voicing mathematics," "personalizing mathematics," "challenging mathematics," and "negotiating mathematics." These concepts are integrated into an overall process for stimulating reflexive discussions and are conceptualized as "unboxing mathematics." The overarching concept of unboxing mathematics represents one interpretation of how reflexive discussions may be constituted during modeling activities and identifies classroom mathematical practices specific to the socio-critical modeling context of this study.

Informed by a constructivist-based, student-adaptive pedagogical approach, this study explores the benefits of teacher-learner discourse moves for the mathematics learning of students with learning disabilities (LD). During a constructivist teaching experiment for nurturing the multiplicative reasoning and problem solving of five third-grade students with school-identified LD, we analyzed the global trends and detailed dynamics of teacher-student interactions with statistical discourse analysis. We found that the teacher's discourse moves to support each individual student's problem solving helped engage them in mathematical reasoning, which improved their problem-solving performance. Thus, this study contributes to knowledge in the field of special education by (a) specifying ways in which discourse-oriented mathematics instruction can help each student with LD and (b) showcasing a novel statistical analysis of teacher-student discourse.

Estimating the difficulty level of math word problems is an important task for many educational applications. Identification of relevant and irrelevant sentences in math word problems is an important step for calculating the difficulty levels of such problems. This paper addresses a novel application of text categorization to identify two types of sentences in "mathematical word problems", namely "relevant" and "irrelevant" sentences. A novel joint probabilistic classification model is proposed to estimate the joint probability of classification decisions for all sentences of a math word problem by utilizing the correlation among all sentences along with the correlation between the question sentence and other sentences, and sentence text. The proposed model is compared with (i) a SVM classifier which makes independent classification decisions for individual sentences by only using the sentence text, and (ii) a novel SVM classifier that considers the correlation between the question sentence and other sentences along with the sentence text. An extensive set of experiments demonstrates the effectiveness of the joint probabilistic classification model for identifying relevant and irrelevant sentences, as well as the novel SVM classifier that utilizes the correlation between the question sentence and other sentences. Furthermore, empirical results and analysis show that (i) it is highly beneficial not to remove stopwords, and (ii) utilizing part of speech tagging does not make a significant improvement although it has been shown to be effective for the related task of math word problem type classification.

The increased relevancy and importance of quantitative literacy (QL) have called for educational reform in undergraduate mathematics course offerings. However, lack of clear guidelines has led to wide variability in the content and outcomes of math courses meant to address QL. Therefore, having an expert consensus regarding content and outcomes for an undergraduate mathematics course focusing on QL would advance the quantitative literacy initiative in higher education. The authors propose the Delphi Method to aid in developing a framework based on expert opinions. The authors explain the Method and suggest the result of this process should yield a research-based framework that faculty and institutions can use as a resource for QL math courses [ABSTRACT FROM AUTHOR]

The Common Core Mathematics Standards have raised expectations for schools and students in the United States. These standards demand much deeper content knowledge from teachers of mathematics and their students. Given the increasingly diverse student population in today's classrooms and shortage of qualified special education teachers, computer-assisted instruction may provide supplementary support, in conjunction with the core mathematics instruction, for meeting the needs of students with different learning profiles. The purpose of this study was to explore the potential effects of the Please Go Bring Me-Conceptual Model-Based Problem Solving (PGBM-COMPS) intelligent tutor program on enhancing the multiplicative problem-solving skills of students with learning disabilities or difficulties in mathematics.