You Can't Compare If You Don't Prepare: Differential Item Functioning In Measures of Grit, STEM Self-Regulation, and Motivation

Gladstone, Jessica R.; Morell, Monica; Yang, Ji Seung; Ponnock, Annette R.; Faust, Lara Turci; Wigfield, Allan

doi:10.1080/00220973.2022.2062584

Cited by 3 publications

(4 citation statements)

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“…Among the studies examined, only one study (1.4%, e.g., Shurygin et al, 2023) used an experimental method to examine motivation in STEM education. students' motivation regarding STEM (e.g., Gok, 2021;Kuo et al, 2019;Stringer et al, 2020;Yang et al, 2018), the effects of instructional activities on participants' motivation on STEM (Asigigan & Samur, 2021;Chittum et al, 2017;Funa et al, 2021;Higde & Aktamis, 2022;Julià & Antolí, 2019;LaForce et al, 2017;Tsai et al, 2021), investigating the relationships between STEM motivation and related factors (Fong et al, 2021;Gladstone et al, 2022;Hermans et al, 2022;Jeong et al, 2020;Jiang et al, 2020;Vennix et al, 2022). Regarding student-level factors, for example, a study by Dokme et al (2022) researched the motivation of female preservice teachers toward their STEM fields.…”

Section: Resultsmentioning

confidence: 99%

Exploring the role of motivation in STEM education: A systematic review

Bayanova

Orekhovskaya

Sokolova

et al. 2023

EURASIA J Math Sci Tech Ed

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Motivation is an essential factor influencing learners’ active participation in STEM subjects and their decision to study STEM fields. This study aimed to determine the current state of research on motivation and STEM and systematically review the current research in the literature. The study examined 78 articles published in journals indexed in the SCOPUS database. Two researchers collected and analyzed the data using the content analysis method. The results showed that the first research on motivation and STEM were published in 2008, and most research papers were published in 2021. The results also showed that most studies were conducted in the United States (47.8%), and the preferred participants were undergraduates (28.1%), high school students (26.7%), and middle school students (14%), respectively. In addition, the results revealed that researchers primarily used the quantitative method to collect data, and a substantial ratio of the studies (83%) investigated student-level factors. Based on the results obtained from this study, it can be concluded that there is a need to comprehensively present the main research results on motivation in STEM education. We suggest that future research should examine databases such as ERIC, ProQuest, and Web of Science and include other documents in the analysis, including book chapters, conference papers, dissertations, and theses.

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Section: Resultsmentioning

confidence: 99%

Exploring the role of motivation in STEM education: A systematic review

Bayanova

Orekhovskaya

Sokolova

et al. 2023

EURASIA J Math Sci Tech Ed

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“…To assess the self‐regulated skills of students such as motivation and learning strategies, we adopted a modified version of the MSLQ originally developed by Pintrich et al (1993) included four subscales: self‐efficacy, intrinsic value, cognitive strategy, and self‐regulation. Researchers have widely employed the MSLQ to measure students' self‐regulation skills due to its reliability and validity (Cheng & Tsai, 2020; Gladstone et al, 2022). The four subscale items are respectively 6,5,6,4, and each item was rated on a 5‐point Likert scale (from 1, fully disagree to 5, fully agree).…”

Section: Methodsmentioning

confidence: 99%

“…To examine students' self‐regulation, researchers widely employed the Motivated Strategies for Learning Questionnaire (MSLQ) developed by Pintrich in 1993, which is considered highly reliable and valid in measuring students' self‐regulatory skills (Cheng & Tsai, 2020; Gladstone et al, 2022; Pintrich et al, 1993). This study employed the short scale of MSLQ, which is classified into two dimensions: learning motivation (self‐efficacy, intrinsic value, and test anxiety) and learning strategies (self‐management strategies and cognitive strategies).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Researchers have widely employed the MSLQ to measure students' self-regulation skills due to its reliability and validity (Cheng & Tsai, 2020;Gladstone et al, 2022). The four subscale items are respectively 6,5,6,4, and each item was rated on a 5-point Likert scale (from 1, fully disagree to 5, fully agree).…”

Section: Motivated Strategies For Learning Questionnairementioning

confidence: 99%

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How do students' self‐regulation skills affect learning satisfaction and continuous intention within desktop‐based virtual reality? A structural equation modelling approach

Liu

et al. 2022

Brit J Educational Tech

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Virtual reality, as an excellent supportive instructional technology, has gained increasing attention from educators and professionals, where desktop‐based virtual reality (DVR) is broadly adopted due to its affordability and accessibility. However, when evaluating students' learning experiences such as flow experiences in DVR environments, most studies adopt a single construct (the total score of flow experience) rather than multiple constructs (enjoyment, engagement, concentration, presence and time distortion). This study implemented desktop‐based virtual reality for a STEM bridge designing program with a total of 254 undergraduates to investigate the relationship between self‐regulation skills, five dimensions of flow experience, learning satisfaction and continuous intention when engaging in a DVR learning environment. The results revealed that self‐regulated learning exerted a dominant impact on students' learning attitudes in DVR learning, in which students' flow experience had a significant mediating effect. Notably, although DVR exhibited poor time distortion, higher satisfaction and continuous intention were still predicted by the mentality of flow experience (ie, enjoyment, engagement, concentration and presence). The findings of this study contribute to the consideration of learning experiences and attitudes, which has insights for the future design of desktop‐based virtual reality environments and related instructional activities. What is already known about this topic Students are different in self‐regulation skills, which influences their satisfaction and continuous intention in learning. Students' self‐regulation skills are one of the important variables in predicting their flow experience. A high level of flow experience contributes to a coherent and efficient learning experience within desktop‐based virtual reality (DVR) environments. What this paper adds Students' self‐regulation skills positively predicted their flow experience and satisfaction in DVR environments. The components of flow experience (enjoyment, concentration and presence) partially mediated the relationship between self‐regulation skills and satisfaction. Students' self‐regulation skills indirectly affect continuous intention by the enjoyment and engagement of flow experiences. Implications for practice and/or policy When delivering DVR‐based learning activities educators should be supportive of students with low levels of self‐regulation skills. Emphasis on promoting flow experiences such as enjoyment, engagement, concentration and presence in designing a DVR‐based classroom could enhance student satisfaction and continuous intention. Embedding scaffolding or feedback in DVR settings would support self‐regulated learning and subsequently improve student satisfaction and persistence through enhanced flow experience.

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An Analysis of DIF and Sources of DIF in Achievement Motivation Items Using Anchoring Vignettes

Bialo,

2024

Educational Assessment

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You Can't Compare If You Don't Prepare: Differential Item Functioning In Measures of Grit, STEM Self-Regulation, and Motivation

Cited by 3 publications

References 42 publications

Exploring the role of motivation in STEM education: A systematic review

Exploring the role of motivation in STEM education: A systematic review

How do students' self‐regulation skills affect learning satisfaction and continuous intention within desktop‐based virtual reality? A structural equation modelling approach

An Analysis of DIF and Sources of DIF in Achievement Motivation Items Using Anchoring Vignettes

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