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Community college students face difficulties in mathematics courses and may not understand the relevance of the topics they are learning to their intended career. When such connections are not made, mathematics courses can become barriers to pursuit of careers in Science, Technology, Engineering, and Mathematics (STEM). In the present study, we assessed student interest in mathematics and various STEM career areas and students’ knowledge of ways mathematics was involved in STEM careers in order to better understand how these variables are related. We discovered that interest in mathematics predicted interest in many, but not all, categories of STEM and STEM-related careers. We also assessed how deeply the student was engaged with their current career pathway, and how this related to other variables. We found that students’ depth of interest in their chosen career path was only associated with mathematics interest for some STEM careers. Finally, students’ perceptions of how mathematics was used in their chosen career area predicted their interest in mathematics, and their interest in some STEM career areas. 

Mathematics is an important tool in engineering practice, as mathematical rules govern many designed systems (e.g., Nathan et al., 2013; Nathan et al., 2017). Investigations of structural engineers suggest that mathematical modelling is ubiquitous in their work, but the nature of the tasks they confront is not well-represented in the K-12 classroom (e.g., Gainsburg, 2006). This follows a larger literature base suggesting that school mathematics is often inauthentic and does represent how mathematics is used in practice. At the same time, algebra is a persistent gatekeeper to careers in engineering (e.g., Harackiewicz et al., 2012; Olson & Riordan, 2012). In the present study, we interviewed 12 engineers, asking them a series of questions about how they use specific kinds of algebraic function (e.g., linear, exponential, quadratic) in their work. The purpose of these interviews was to use the responses to create mathematical scenarios for College Algebra activities that would be personalized to community college students’ career interests. This curriculum would represent how algebra is used in practice by STEM professionals. However, our results were not what we expected. In this paper, we discuss three major themes that arose from qualitative analyses of the interviews. First, we found that engineers resoundingly endorsed the importance of College Algebra concepts for their day-to-day work, and uniformly stated that math was vital to engineering. However, the second theme was that the engineers struggled to describe how they used functions more complex than linear (i.e., y=mx+b) in their work. Students typically learn about linear functions prior to College Algebra, and in College Algebra explore more complex functions like polynomial, logarithmic, and exponential. Third, we found that engineers rarely use the explicit algebraic form of an algebraic function (e.g., y=3x+5), and instead rely on tables, graphs, informal arithmetic, and computerized computation systems where the equation is invisible. This was surprising, given that the bulk of the College Algebra course involves learning how to use and manipulate these formal expressions, learning skills like factoring, simplifying, solving, and interpreting parameters. We also found that these trends for engineers followed trends we saw in our larger sample where we interviewed professionals from across STEM fields. This study calls into question the gatekeeping role of formal algebraic courses like College Algebra for STEM careers. If engineers don’t actually use 75% of the content in these courses, why are they required? One reason might be that the courses are simply outdated, or arguments might be made that learning mathematics builds more general modelling and problem-solving skills. However, research from educational psychology on the difficulty of transfer would strongly refute this point – people tend to learn things that are very specific. Another reason to consider is that formal mathematics courses like advanced algebra have emerged as a very convenient mechanism to filter people by race, gender, and socioeconomic background, and to promote the maintenance of the “status quo” inequality in STEM fields. This is a critical issue to investigate for the future of the field of engineering as a whole. 

Teachers, schools, districts, states, and technology developers endeavor to personalize learning experiences for students, but definitions of personalized learning (PL) vary and designs often span multiple components. Variability in definition and implementation complicate the study of PL and the ways that designs can leverage student characteristics to reliably achieve targeted learning outcomes. We document the diversity of definitions of PL that guide implementation in educational settings and review relevant educational theories that could inform design and implementation. We then report on a systematic review of empirical studies of personalized learning using PRISMA guidelines. We identified 376 unique studies that investigated one or more PL design features and appraised this corpus to determine (1) who studies personalized learning; (2) with whom, and in what contexts; and (3) with focus on what learner characteristics, instructional design approaches, and learning outcomes. Results suggest that PL research is led by researchers in education, computer science, engineering, and other disciplines, and that the focus of their PL designs differs by the learner characteristics and targeted outcomes they prioritize. We further observed that research tends to proceed without a priori theoretical conceptualization, but also that designs often implicitly align to assumptions posed by extant theories of learning. We propose that a theoretically guided approach to the design and study of PL can organize efforts to evaluate the practice, and forming an explicit theory of change can improve the likelihood that efforts to personalize learning achieve their aims. We propose a theory-guided method for the design of PL and recommend research methods that can parse the effects obtained by individual design features within the “many-to-many-to-many” designs that characterize PL in practice. 

This article introduces a special issue comprising research on efforts to personalize learning in different academic subjects. We first consider the emergence of personalized learning (PL) and the myriad of definitions that describe its essential features. Thereafter, we introduce the articles in the special issue by examining their alignment to extant theories of learning, the instructional design features that personalize the learning experience based on a learner characteristic, and the relationships between PL design and outcomes achieved in an educational context. Based on observations of contemporary PL research, we identify key issues to be addressed by the field and recommendations for future researchers to undertake to advance a PL theory. Chief among issues with PL are the role of technology, the agency of the learner, and the absence of a consistent theoretical grounding to motivate PL design choices. Future directions that would advance PL include the adoption of a theory of change in PL design, a design-based research approach to refine PL initiatives, more intensive and iterative research in authentic classroom contexts, and a greater focus on student input into and ownership of the PL experience. 

Mathematics experienced by students can be derived from the contextually situated “real world” experiences of the educator, which is typically White and middle class and not a reflection of the demographics of many classrooms in the United States. Activities where students find connections to their lives and interests have shown promise in enhancing student performance and experiences in mathematics classrooms. In this study, mathematics funds of knowledge are assessed in a novel survey instrument, reinforcing the salience of relating math experiences to students’ lives and acknowledging skills and knowledge originating from experiences outside of the math classroom. 

Survey data for community college algebra students reveals relationships between a student’s attitudes towards mathematics and the student’s STEM career interests. Results show that while students may not always have a clear understanding of the tasks related to a chosen STEM career area, the student’s math interest predicts interest in some STEM careers and not others.