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Relating students' motivation trajectories in the first nine weeks of their vocational education program to dropout and persistence.

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Title: Relating students' motivation trajectories in the first nine weeks of their vocational education program to dropout and persistence.
Authors: Eegdeman, Irene (AUTHOR), Plak, Simone (AUTHOR), Meeter, Martijn (AUTHOR)
Source: Motivation & Emotion. Aug2025, Vol. 49 Issue 4, p364-383. 20p.
Subjects: Academic motivation, Persistence (Personality trait), Longitudinal method, Vocational education, Social integration, School failure, Motivation (Psychology), Student engagement
Abstract: To discern early indicators of student attrition, this study examines the dynamics of motivation in the first weeks of a vocational education program. Using a longitudinal approach, freshmen rated on a weekly basis the intrinsic value of their program, expectations of success, their mastery and achievement orientation, and academic and social integration. By comparing responses between eventual dropouts and retained students, the study confirms previous findings that right at the start of the program the distinction between the two groups is minimal. However, motivational differences emerged as early as the second and third weeks, pointing to the potential usefulness of the questionnaire as an early indicator of dropout risk, and of interventions that target motivation early in vocational education programs to reduce dropout rates. [ABSTRACT FROM AUTHOR]
Copyright of Motivation & Emotion is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="DE" term="%22Academic+motivation%22">Academic motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Persistence+%28Personality+trait%29%22">Persistence (Personality trait)</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Vocational+education%22">Vocational education</searchLink><br /><searchLink fieldCode="DE" term="%22Social+integration%22">Social integration</searchLink><br /><searchLink fieldCode="DE" term="%22School+failure%22">School failure</searchLink><br /><searchLink fieldCode="DE" term="%22Motivation+%28Psychology%29%22">Motivation (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink>
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  Data: To discern early indicators of student attrition, this study examines the dynamics of motivation in the first weeks of a vocational education program. Using a longitudinal approach, freshmen rated on a weekly basis the intrinsic value of their program, expectations of success, their mastery and achievement orientation, and academic and social integration. By comparing responses between eventual dropouts and retained students, the study confirms previous findings that right at the start of the program the distinction between the two groups is minimal. However, motivational differences emerged as early as the second and third weeks, pointing to the potential usefulness of the questionnaire as an early indicator of dropout risk, and of interventions that target motivation early in vocational education programs to reduce dropout rates. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Motivation & Emotion is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11031-025-10127-z
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        Text: English
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      – SubjectFull: Longitudinal method
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              Text: Aug2025
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