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Model 6 (M6) was used as the basis for the analysis of how the three background variables (gender, parent education, and student's long term educational aspirations) and students' mathematics achievements were related to the motivational constructs.
For the students' long term educational aspirations (thereafter aspirations)—aspiring to higher levels of education—have been found to significantly correlate with higher performance (e.g., Gil-Flores et al. 2011; Marjoribanks 2002, 2003a, b).
The relationship between students' long term educational aspiration and achievement (Mdn r = 0.444) was stronger than any other measure in all the countries.
However, because of the diverse cultural background of the students' in our study we leave open the question of how students long term educational aspirations relate to achievement and the motivational constructs.
Abrams demonstrates that for-profit schools have no incentives to consider long term educational or social goals.
Twins seem to be at long term educational disadvantage.
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Moreover, parental education, gender and long-term educational aspiration also influence the students' achievement and motivation.
As the Chinese government addresses its long-term educational development, with emphasis on equitable and quality education for all, the CFS model is increasingly being mainstreamed into national policy and legislative changes.
Gender (coded 1 for girls, 2 for boys); Long-term educational aspirations participants were required to indicate the highest level of education they expected to attain on a scale ranging from (1) "Lower secondary education" to (6) "University program- Master/Doctorate" (See Foy et al. 2013 for specific nationally definition classifications).
Long-term educational aspirations participants were required to indicate the highest level of education they expected to attain on a scale ranging from (1) "Lower secondary education" to (6) "University program- Master/Doctorate" (See Foy et al. 2013 for specific nationally definition classifications).
It will allow more nimble and rapid responses to fast-moving emergencies and, at the same time, support long-term educational viability and growth.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com