Investigating bifactor modeling of biology undergraduates’ task values and achievement goals across semesters.
Investigating bifactor modeling of biology undergraduates’ task values and achievement goals across semesters.
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研究生物本科生跨学期任务价值和成就目标的双因素建模。
DOI：
10.1037/edu0000803
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发表时间：
2023
影响因子：
4.9
通讯作者：
Panter, Abigail T.
中科院分区：
文献类型：
--
作者：
Greene, Jeffrey A.;Bernacki, Matthew L.;Plumley, Robert D.;Kuhlmann, Shelbi L.;Hogan, Kelly A.;Evans, Mara;Gates, Kathleen M.;Panter, Abigail T.
本科科学、技术、工程和数学(STEM)学生的动机对他们是否以及如何在具有挑战性的课程和STEM职业生涯中坚持下去有着很大的影响。对动机结构的适当概念和测量，如学生对价值和成本的期望和感知(即期望价值理论[EVT])以及他们的目标(即成就目标理论[AGT])，对于理解和提高STEM的持久性和成功是必要的。研究结果表明，探索动机结构的多种测量模型很重要，包括传统的验证性因素分析、探索性结构方程模型(ESEM)和双因素模型，但还需要更多的研究来确定相同的模型是否最适合不同时间和背景。因此，我们测量了本科生的EVT和AGT动机，并调查了哪种测量模型最符合数据，以及测量的不变性是否成立，跨越了三个学期。在确定了最合适的测量模型和不变性类型后，我们使用表现最好的模型中的分数来预测生物学成绩。测量结果表明，双因素-ESEM模型具有最好的EVT数据模型，ESEM模型最适合AGT的数据模型，且有学期测量不变性的证据。动机因素，特别是成就价值和主观任务价值，预测了每个学期生物课程结果的微小但统计上显著的差异。我们的发现为使用现代测量模型捕捉学生的STEM动机提供了支持，并潜在地完善了他们的概念化。这样的未来研究将提高教育工作者善意地监控和支持学生动机的能力，并提高STEM的表现和职业成功。
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 (ie, expectancy value theory [EVT]) and their goals (ie, 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.(PsycInfo Database Record (c) 2023 APA, all rights reserved)