Investigating underrepresented and first‐generation college students' science and math motivational beliefs: A nationally representative study using latent profile analysis

Abstract

The purpose of this study was to investigate the math and science beliefs of underrepresented students, with a focus on first-generation college (FGC) students. We do so by estimating a typology of high school students based on their mathematics and science beliefs and examining which group(s) students were more likely to be in. We used latent profile analysis and a nationally representative sample of high school students from the High School Longitudinal Study of 2009. We identified four groups, low-all, medium-low science/medium-high math, medium-high science/medium-low math, and high-all. Among these four groups, FGC students were overrepresented among students in the low-all group, which had negative math and science beliefs and the worst math, science, and STEM (science, technology, engineering, and mathematics) outcomes. Our findings have for research on FGC students as well as for practitioners who work with them.

Are students in Italy really disinterested in science? A person‐centered approach using the PISA 2015 data

“…Conversely, the person‐centered approach assumes the existence of these subgroups, allowing for patterns to be observed within and across populations or reconfirmed across groups belonging to different contexts (Bergman & Trost, 2006; Magnusson, 2003), thus complementing the findings from the former (Fryer & Bovee, 2018). Studies using the person‐centered approach are on the rise and have addressed a variety of topics (e.g., Chen, 2012; Fryer & Ainley, 2019; Kampa et al, 2016; Schmidt et al, 2018; Snodgrass Rangel et al, 2020; Wormington & Linnenbrink‐Garcia, 2017). Despite this, the field is still very much lacking in attempts that address the different phenomena related to how students experience science from a person‐centered perspective and which of these experiences and beliefs may be facilitating in students becoming competent participants of scientific practice (Aditomo & Klieme, 2020).…”

“…However, more inquiry is still needed to determine which of the influences between self‐efficacy, interest, engagement, and particular contexts are shared between student subcategories (e.g., boys and girls) and which are distinctive to each of these groups (Fredricks et al, 2018). It is also crucial to uncover any qualitative differences (e.g., motivational patterns between first‐ and second‐generation students; Alivernini et al, 2018 or first‐generation and majority population; Snodgrass Rangel et al, 2020). Examining all these aspects can continue to foster our understanding of how particular learning environments may be even more supportive in the process of building a highly competent individual engaged in scientific practice later in life (Aditomo & Klieme, 2020; Freeman et al, 2015; Weeth Feinstein & Kirchgasler, 2015).…”

“…Lately, this has been further strengthened by research on instructional practices and achievement (e.g., Lau & Lam, 2017) and that examining students’ self‐beliefs in connection to instructional approaches (Areepattamannil et al, 2020). Nonetheless, scholars have still not demonstrated a unified portrait of the domain, and the extent particular relationships may be salient to distinctive student subgroups (Alivernini et al, 2018; Fredricks et al, 2018; Hayenga & Corpus, 2010; Snodgrass Rangel et al, 2020). One reason for this may lie in the dominance of the variable‐centered approach itself because it disregards the possible differences between individuals whose measures are being used in evaluating effects and associations (Bergman & Trost, 2006; Magnusson, 2003).…”

“…Relevant to the context of science, examples may be found in the works of Chen (2012) and Kampa et al (2016), both of whom rely heavily on the use of LPAs that observe epistemological beliefs related to science. She et al (2019), using the same approach observe scientific literacy, epistemological beliefs, and affective dispositions, while Snodgrass Rangel et al (2020) examine math and science beliefs of underrepresented students, with a focus on first‐generation college students. Schmidt et al (2018), on the other hand, rely on the more traditional clustering techniques (i.e., hierarchical cluster analyses and k‐means analyses) in discerning what they label “momentary engagement profiles”.…”

“…(1) Which student profiles can be distinguished when using measures on enjoyment of science, interest in science, instrumental motivation related to science, science self‐efficacy, involvement in different science activities, and science epistemological beliefs? Based on studies considering similar person‐centered perspectives (e.g., Chen, 2012, Kampa et al, 2016, Schmidt et al, 2018, She et al, 2019, Snodgrass Rangel et al, 2020), the existence of distinctive profiles that combine both adaptive and less adaptive aspects across a number of subgroups and background research on the construct embedded in the profiles (e.g., Alivernini & Manganelli, 2015; Chen et al, 2019; Conley et al, 2004; Palmer et al, 2017; Fredricks et al, 2018; Grabau & Ma, 2017; Jansen et al, 2015; Jeffries et al, 2020; Lam & Lau, 2014; Todd & Zvoch, 2019), we hypothesize the existence of these mixed students profiles as well.…”