Educational aspirations in inner city schools

The research aimed to assess the nature and level of pupils’ educational aspirations and to elucidate the factors that influence these aspirations. A sample of five inner city comprehensive secondary schools were selected by their Local Authority because of poor pupil attendance, below average examination results and low rates of continuing in full-time education after the age of 16. Schools were all ethnically mixed and co-educational. Over 800 pupils aged 12-14 completed a questionnaire assessing pupils’ experience of home, school and their peers. A sub-sample of 48 pupils selected by teachers to reflect ethnicity and ability levels in individual schools also participated in detailed focus group interviews. There were no significant differences in aspirations by gender or year group, but differences between ethnic groups were marked. Black African, Asian Other and Pakistani groups had significantly higher educational aspirations than the White British group, who had the lowest aspirations. The results suggest the high aspirations of Black African, Asian Other and Pakistani pupils are mediated through strong academic self-concept, positive peer support, a commitment to schooling and high educational aspirations in the home. They also suggest that low educational aspirations may have different mediating influences in different ethnic groups. The low aspirations of White British pupils seem to relate most strongly to poor academic self-concept and low educational aspirations in the home, while for Black Caribbean pupils disaffection, negative peers and low commitment to schooling appear more relevant. Interviews with pupils corroborated the above findings and further illuminated the factors students described as important in their educational aspirations. The results are discussed in relation to theories of aspiration which stress its nature as a cultural capacity

Modelling the dynamics of the students academic performance in the German region of North Rhine- Westphalia: an epidemiological approach with uncertainty

This is an author's accepted manuscript of an article published in "International Journal of Computer Mathematics"; Volume 91, Issue 2, 2014; copyright Taylor & Francis; available online at: http://dx.doi.org/10.1080/00207160.2013.813937Student academic underachievement is a concern of paramount importance in Europe, where around 15% of the students in the last high school courses do not achieve the minimum knowledge academic requirement. In this paper, we propose a model based on a system of differential equations to study the dynamics of the students academic performance in the German region of North Rhine-Westphalia. This approach is supported by the idea that both, good and bad study habits, are a mixture of personal decisions and influence of classmates. This model allows us to forecast the student academic performance by means of confidence intervals over the next few years.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness grant MTM2009-08587 and Universitat Politecnica de Valencia grant PAID06-11-2070.Cortés, J.; Ehrhardt, M.; Sánchez Sánchez, A.; Santonja, F.; Villanueva Micó, RJ. (2014). Modelling the dynamics of the students academic performance in the German region of North Rhine- Westphalia: an epidemiological approach with uncertainty. International Journal of Computer Mathematics. 91(2):241-251. https://doi.org/10.1080/00207160.2013.813937S241251912Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 21(1), 243-247. doi:10.1007/bf02532251Brockwell, P. J., & Davis, R. A. (1996). Introduction to Time Series and Forecasting. Springer Texts in Statistics. doi:10.1007/978-1-4757-2526-1Dogan, G. (2007). Bootstrapping for confidence interval estimation and hypothesis testing for parameters of system dynamics models. System Dynamics Review, 23(4), 415-436. doi:10.1002/sdr.362Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1), 1-26. doi:10.1214/aos/1176344552LJUNG, G. M., & BOX, G. E. P. (1979). The likelihood function of stationary autoregressive-moving average models. Biometrika, 66(2), 265-270. doi:10.1093/biomet/66.2.265Martcheva, M., & Castillo-Chavez, C. (2003). Diseases with chronic stage in a population with varying size. Mathematical Biosciences, 182(1), 1-25. doi:10.1016/s0025-5564(02)00184-0J.D. Murray,Mathematical Biology, Springer, New York, 2002.Nelder, J. A., & Mead, R. (1965). A Simplex Method for Function Minimization. The Computer Journal, 7(4), 308-313. doi:10.1093/comjnl/7.4.308Yazici, B., & Yolacan, S. (2007). A comparison of various tests of normality. Journal of Statistical Computation and Simulation, 77(2), 175-183. doi:10.1080/10629360600678310M.Á.M. Zabal, P.F. Berrocal, C. Coll, and M. de los Ángeles Melero Zabal,La Interacción Social en Contextos Educativos[Social interaction in educational contexts], Psicología/Siglo XXI de España Editores Series, Siglo XXI de España, 1995

Collaborative Training With a More Experienced Partner: Remediating Low Pretraining Self-Efficacy in Complex Skill Acquisition

Objective: This study examined the effectiveness of collaborative training for individuals with low pretraining self-efficacy versus individuals with high pretraining selfefficacy regarding the acquisition of a complex skill that involved strong cognitive and psychomotor demands. Background: Despite support for collaborative learning from the educational literature and the similarities between collaborative learning and interventions designed to remediate low self-efficacy, no research has addressed how selfefficacy and collaborative learning interact in contexts concerning complex skills and human-machine interactions. Method: One hundred fifty-five young male adults trained either individually or collaboratively with a more experienced partner on a complex computer task that simulated the demands of a dynamic aviation environment. Participants also completed a task-specific measure of self-efficacy before, during, and after training. Results: Collaborative training enhanced skill acquisition significantly more for individuals with low pretraining self-efficacy than for individuals with high pretraining self-efficacy. However, collaborative training did not bring the skill acquisition levels of those persons with low pretraining self-efficacy to the levels found for persons with high pretraining self-efficacy. Moreover, tests of mediation suggested that collaborative training may have enhanced appropriate skill development strategies without actually raising self-efficacy. Conclusion: Although collaborative training can facilitate the skill acquisition process for trainees with low self-efficacy, future research is needed that examines how the negative effects of low pretraining self-efficacy on complex skill acquisition can be more fully remediated. Application: The differential effects of collaborative training as a function of self-efficacy highlight the importance of person analysis and tailoring training to meet differing trainee needs.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline