How Dimensional Comparisons Help to Understand Linkages Between Expectancies, Values, Performance, and Choice

Wigfield, Allan; Eccles, Jacquelynne S.; Möller, Jens

doi:10.1007/s10648-020-09524-2

Cited by 66 publications

(59 citation statements)

References 59 publications

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“…In a recent extension of this theory (the generalized I/E model; Möller et al, 2015 , 2016 ), Möller and colleagues have proposed that individuals might use any psychologically relevant construct for dimensional comparison. Initial support for this was found with teacher support in school subjects (Dietrich et al, 2015 ), and more recently subjective task values have also been argued to be relevant in dimensional comparisons (Wigfield et al, 2020 ). The present study focused on anticipated belonging, because this is a particularly relevant construct to understand women’s underrepresentation in STEM in general, and because a focus on achievement is not enough to understand women’s underrepresentation in STEM (Master et al, 2016 ; Wang et al, 2013 ).…”

Section: Discussionmentioning

confidence: 96%

“…First research has indeed found support for this by showing that higher levels of perceived teacher support in one school subject was related to lower motivation in another subject (Dietrich et al, 2015 ). Following these findings, it was argued that any psychologically relevant construct in a domain can be used in dimensional comparisons (Möller et al, 2015 , 2016 ; see also Wigfield et al, 2020 ). Furthermore, dimensional comparison theory has also been expanded to other domains besides the verbal subject and math, such as physics and chemistry.…”

Section: Introductionmentioning

confidence: 99%

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“Where will I belong more?”: The role of belonging comparisons between STEM fields in high school girls’ STEM interest

et al. 2021

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In trying to understand women’s underrepresentation in STEM (Science, Technology, Engineering, Mathematics), most existing research focuses on one STEM-field or collapses across all STEM-fields. However, these fields differ vastly in female representation: women tend to be most strongly underrepresented in technological and computer science university majors and to a lesser extent in mathematics and chemistry, while they are less underrepresented in biological sciences. To understand this variability, we examine how girls in the process of making higher education choices compare different STEM-fields to each other. We draw upon dimensional comparison theory, which argues that educational motivation involves intra-individual comparisons of achievement across school subjects. However, previous research has shown that a focus on achievement in STEM is not enough, anticipated belonging in a STEM-field plays a pivotal role in interest in pursuing that field. Consistent with this, we examined participants’ comparisons of anticipated belonging across STEM-fields. A sample of 343 high school girls in STEM-focused university tracks completed a survey on their anticipated belonging and interest in pursuing different STEM majors. Latent Profile Analysis resulted in 3 profiles, showing different belonging comparison patterns across STEM-fields. Examining these comparisons—both within and across profiles—showed how girls felt pushed away from certain STEM-fields and pulled toward others. The findings suggest that for interest in pursuing specific STEM-fields it is not just about the level of anticipated belonging within that STEM-field, but just as much about the level of anticipated belonging in comparison to another STEM-field. Supplementary Information The online version contains supplementary material available at 10.1007/s11218-021-09663-6.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

“Where will I belong more?”: The role of belonging comparisons between STEM fields in high school girls’ STEM interest

et al. 2021

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“…Expectancies and values have a direct influence on achievement choices, performance, effort, and persistence (Wigfield & Eccles, 2000). Different components of achievement values have been defined: (a) attainment value or importance of doing well, (b) intrinsic value or enjoyment from doing the task, (c) utility value or usefulness of the task, and (d) perceived cost to finish the task (Wigfield et al, 2020).…”

Section: Literature Review Motivationmentioning

confidence: 99%

Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?

Luik

Lepp

2021

IRRODL

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Computer programming MOOCs attract people who have different motivations. Previous studies have hypothesized that the motivation declared before starting the course can be an important predictor of distinctive dropout rates. The aim of this study was to outline the main motivation clusters of participants in a computer programming MOOC, and to compare how these clusters differed in terms of intention to complete and actual completion rate. The sample consisted of 1,181 respondents to the pre-course questionnaire in the Introduction to Programming MOOC. A validated motivation scale, based on expectancy-value theory and k-means cluster analysis, was used to form the groups. The four identified clusters were named as Opportunity motivated (27.7%), Over-motivated (28.6%), Success motivated (19.6%) and Interest motivated (24.0%). Comparison tests and chi-square test were used to describe the differences among the clusters. There were statistically significant differences among clusters in self-evaluated probability of completion. Also, significant differences emerged among three clusters in terms of percentages of respondents who completed the MOOC. Interestingly, the completion rate was the lowest in the Over-motivated cluster. A statistically significant higher ratio of completers to non-completers was found in the Opportunity motivated, Success motivated, and Interest motivated clusters. Our findings can be useful for MOOC instructors, as a better vision of participants’ motivational profiles at the beginning of the MOOC might help to inform the MOOC design to better support different needs, potentially resulting in lower dropout rates.

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“…In a recent article, Wigfield et al (2020) connected DCT with SEVT (Eccles, 1983; Eccles & Wigfield, 2020; Wigfield et al, 2006). SEVT provides a framework for explaining individual educational engagement, performance, and choice.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

What Matters When? Social and Dimensional Comparisons in the Context of University Major Choice

Keyserlingk¹,

Dicke

Becker

et al. 2021

AERA Open

Self Cite

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Students compare their achievement to different standards in order to evaluate their ability. We built on the theoretical frameworks of situated expectancy-value theory, dimensional comparison theory, and the big-fish-little-pond effect literature to examine the role of social and dimensional comparisons for ability self-concept and subjective task value (STV) in secondary school and university major choice. We used two German longitudinal data sets from different cohorts with data collection in 12th grade and 2 years after high school graduation (Study 1: N = 2,207, Study 2: N = 1,710). Dimensional and social comparisons predicted students’ self-concept and domain-specific STV in school: Individual achievement was positively related to ability self-concept and STV in the corresponding domain and negatively related in the noncorresponding domain. School-level mean achievement was negatively related to ability self-concept and STV in the corresponding domain. Dimensional comparisons were directly related to university major choice, social comparisons were only indirectly related.

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How Dimensional Comparisons Help to Understand Linkages Between Expectancies, Values, Performance, and Choice

Cited by 66 publications

References 59 publications

“Where will I belong more?”: The role of belonging comparisons between STEM fields in high school girls’ STEM interest

“Where will I belong more?”: The role of belonging comparisons between STEM fields in high school girls’ STEM interest

Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?

What Matters When? Social and Dimensional Comparisons in the Context of University Major Choice

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