Search

Transition to higher education is increasingly becoming a common stage in young adulthood, which highlights the importance of studying what could contribute for a better adaptation to higher education. The aim of this study was to explore the relationship between academic expectations, self-efficacy, and adaptation to higher education during the first two years of college (i.e., the first two years of a higher education degree). Portuguese college students participated in a longitudinal data collection resorting to the Academic Expectations Questionnaire (T1 and T3), the Self-Efficacy in Higher Education Scale (T2 and T3), and the Questionnaire for Higher Education Adaptation (T2 and T3). Structural equation modeling analysis was conducted to test a model correlating academic expectations, self-efficacy, and adaptation, as well as a mediation model where a full mediation of self-efficacy was observed between the relationship between academic expectations and higher education expectations. This study delivers a unique longitudinal view on the experience of the first two years of college, showing a significant role of expectations and self-efficacy in order to achieve a better adaptation process. Results are useful for institutions to adapt the way they present themselves and manage students' expectations. [ABSTRACT FROM AUTHOR]

Copyright of Avaliação Psicológica is the property of Instituto Brasileiro de Avaliacao Psicologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. 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.)

The quality of learning in Higher Education is particularly dependent on students' skills in regulating their cognition. This regulation requires cognitive and metacognitive skills as well as motivational dimensions. Due to its relevance in explaining students' academic achievement and developing lifelong learning skills, it's important to increase research in the area. This study aims to adapt and validate a short version of the Regulation of Cognition of Metacognitive Awareness Inventory to firstyear Portuguese university students. A sample of 360 students was considered and was identified a three-dimensional structure (Planning, 4 items; Strategies, 7 items; and Monitoring and evaluation, 7 items) with a secondorder factor (Regulation of Cognition). The internal consistency values of the reduced scale are within the acceptable parameters for a self-report scale and the correlations with academic achievement at the end of the first year of the university guarantee the predictive validity of the scale. This short version of regulation of cognition measure allows its use in research with other instruments in larger studies and can function as a diagnostic / screening tool to help students in higher education learning challenges. [ABSTRACT FROM AUTHOR]

We evaluated the predictive classification accuracy of discriminant analysis, logistic regression and four neural network typologies (multiple layer perceptrons, radial basis networks, probabilistic neural networks, and linear neural networks) on a flight screening program with a pass–fail criterion using several psychometric tests as predictors. A stepwise (for logistic regression and discriminant analysis) and sensitivity (for neural networks) selection procedure identified spatial visualization, eye–hand–foot coordination, and concentration capacity as significant predictors. Performance on the first few flights of the screening program was also retained as a significant predictor of final score. Regarding the accuracy of predictions, logistic regression showed the highest accuracy (77%), with high sensitivity (92%) but low specificity (31%). Discriminant analysis had high sensitivity (77%) and high specificity (64%). However, it had the second lowest accuracy (74%). The best performing neural network type was the multiple layer perception, which showed high sensitivity (85%), the second highest specificity (47%), and high accuracy (76%). Radial basis networks and probabilistic networks both fail to predict correctly the candidates who fail on the flight screening program (0% specificity). [ABSTRACT FROM AUTHOR]