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    Investigation of relationship between prospective teachers' learning beliefs and state of individual innovativeness
    (Birlesik Dunya Yenilik Arastirma ve Yayıncilik Merkezi, 2020) Kinay, İsmail; Suer, Sedef
    Individual innovativeness is one of the most appreciated attributes of 21st-century skills which is needed in every field of daily life. Because of this appreciation, a lot of effort has been spent towards prompting individual innovativeness levels of both students and teachers in teaching and learning environments via innovative practices. Therefore, this study aimed to examine the relationship between prospective teachers' learning beliefs and their individual innovativeness state. In this study, the correlational survey method was used, and the sample of the study was comprised of 515 prospective teachers. The data of the study were collected via the 'Belief Scale towards Learning' and 'Individual Innovativeness Scale'. The data of this study were analysed using the SPSS program. Test of normality, descriptive statistics, correlational analysis and partial linear regression analysis were used to analyse the data. The results of the analysis showed that prospective teachers have a high level of constructivist and a moderate level of traditional learning beliefs while their individual innovativeness state was determined within the category of interrogators. In addition, prospective teachers' beliefs in constructivist learning were determined to be a significant predictor of their individual innovativeness state.
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    Öğe
    Predicting Academic Self-Efficacy Based on Self-Directed Learning and Future Time Perspective
    (Sage Publications Inc, 2023) Karatas, Kasim; Arpaci, Ibrahim; Suer, Sedef
    The purpose of this study was to investigate the relationship between teacher candidates' academic self-efficacy, self-directed learning, and future time perspective. A dual-stage analytical approach, utilizing both traditional structural equation modeling (SEM) and Machine Learning Classification Algorithms, was employed to test the proposed hypotheses. The study included a sample of 879 teacher candidates. The SEM analysis revealed that self-directed learning had a significant positive effect on academic self-efficacy. Furthermore, future time perspective was found to significantly predict academic self-efficacy. The combined endogenous constructs accounted for a substantial portion of the explained variance. Additionally, the study employed LMT and Multiclass classifiers from Machine Learning algorithms to predict academic self-efficacy. In summary, the findings of this study suggest that self-directed learning and future time perspective are significant factors in predicting teacher candidates' academic self-efficacy. The study utilized both traditional SEM and Machine Learning algorithms to provide a comprehensive analysis of the relationships between these variables.