Investigating bifactor modeling of biology undergraduates’ task values and achievement goals across semesters.

Abstract

Undergraduate science, technology, engineering, and mathematics (STEM) students’ motivations have a strong influence on whether and how they will persist through challenging coursework and into STEM careers. Proper conceptualization and measurement of motivation constructs, such as students’ expectancies and perceptions of value and cost (i.e., expectancy value theory [EVT]) and their goals (i.e., achievement goal theory [AGT]), are necessary to understand and enhance STEM persistence and success. Research findings suggest the importance of exploring multiple measurement models for motivation constructs, including traditional confirmatory factor analysis, exploratory structural equation models (ESEM), and bifactor models, but more research is needed to determine whether the same model fits best across time and context. As such, we measured undergraduate biology students’ EVT and AGT motivations and investigated which measurement model best fit the data, and whether measurement invariance held, across three semesters. Having determined the best-fitting measurement model and type of invariance, we used scores from the best performing model to predict biology achievement. Measurement results indicated a bifactor-ESEM model had the best data-model fit for EVT and an ESEM model had the best data-model fit for AGT, with evidence of measurement invariance across semesters. Motivation factors, in particular attainment value and subjective task value, predicted small yet statistically significant amounts of variance in biology course outcomes each semester. Our findings provide support for using modern measurement models to capture students’ STEM motivations and potentially refine conceptualizations of them. Such future research will enhance educators’ ability to benevolently monitor and support students’ motivation, and enhance STEM performance and career success.

Why should you believe in yourself? Students' performance‐approach goals shape their approach to learning through self‐efficacy: A longitudinal analysis

“…Furthermore, in preparing for an increasingly demanding labour market, pursuing performance goals based on surpassing others (i.e., nPAGs) seems much more adaptive (Grosemans et al, 2018). Previous research has focused on achievement goals concerning students' approach to learning or SE (Bonsaksen et al, 2017;Gerhardt & Brown, 2006;Greene et al, 2023;Shim & Ryan, 2005), but little has explored the mechanism of interaction between variables in a longitudinal perspective (Dull et al, 2015;Grosemans et al, 2018). Consequently, the present research explored the relationships between the concepts at three different moments (T1, T2, and T3).…”

“…If students believe competence is related to comparing with others, they tend to adopt performance goals to demonstrate this (Nieminen et al, 2021). Success in achieving goals can enhance self-efficacy (Greene et al, 2023), and belief in their competencies shapes how they approach learning (Fryer, 2017). Students with high self-efficacy are more interested in challenging tasks, use deep processing strategies in learning materials, invest more effort, and persevere in achieving goals (Nieminen et al, 2021).…”

“…Students who believe competence is reflected by comparing with others will probably adopt performance goals (Chung et al, 2020;Huang, 2013). Success in achieving goals increases confidence in their competence and SE (Geitz et al, 2016;Greene et al, 2023). SE in academic tasks is also related to intrinsic and extrinsic motivation, which are basic constructs in SAL (Asikainen & Gijbels, 2017), with high SE facilitating DA and low SE stimulating SA (Herrmann et al, 2017;Nieminen et al, 2021).…”