A Soft Computing Approach to Quality Evaluation of General Chemistry Learning in Higher Education

In contemporary societies higher education must shape individuals able to solve problems in a workable and simpler manner and, therefore, a multidisciplinary view of the problems, with insights in disciplines like psychology, mathematics or computer science becomes mandatory. Undeniably, the great challenge for teachers is to provide a comprehensive training in General Chemistry with high standards of quality, and aiming not only at the promotion of the student’s academic success, but also at the understanding of the competences/skills required to their future doings. Thus, this work will be focused on the development of an intelligent system to assess the Quality-of-General-Chemistry-Learning, based on factors related with subject, teachers and students.

Assessing the Role of General Chemistry Learning in Higher Education

The inclusion of General Chemistry (GC) in the curricula of higher education courses in science and technology aims, on the one hand, to develop students' skills necessary for further studies and, on the other hand, to respond to the need of endowing future professionals of knowledge to analyze and solve multidisciplinary problems in a sustainable way. The participation of students in the evaluation of the role played by the GC in their training is crucial, and the analysis of the results can be an essential tool to increase success in the education of students and improving practices in various professions. Undeniably, this work will be focused on the development of an intelligent system to assess the role of GC. The computational framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a problem solving methodology moored on Artificial Neural Networks. The results so far obtained show that the proposed model stands for a good start, being its overall accuracy higher than 95%.

Self-report depressive symptoms do not directly predict suicidality in nonclinical individuals: Contributions toward a more psychosocial approach to suicide risk

Although suicidality is associated with mental illness in general and depression in particular, many depressed individuals do not attempt suicide and some individuals who attempt to or do die by suicide do not present depressive symptoms. This article aims to contribute to a more psychosocial approach to understanding suicide risk in nonclinical populations. In advocating a psychosocial perspective rather than a depression-focused approach, this article presents four diverse studies that demonstrate sampling and measurement invariance in findings across different populations and specific measures. Study 1 tests the mediation effects of 2 interpersonal variables, thwarted belongingness and perceived burdensomeness, in the association between depressive symptoms and recent suicidality. Studies 2 and 3 evaluate the contribution of hopelessness and psychache, beyond depressive symptoms, to suicidality. Study 4 tests the contribution of life events behind depressive symptoms, and other relevant sociodemographic and clinical variables, to the estimation of “future suicidality.” Overall, results demonstrate that depressive symptoms do not directly predict suicidality in nonclinical individuals, but that other psychosocial variables mediate the association between depressive symptoms and suicidality or predict suicidality when statistically controlling for depressive symptoms. The article contributes to understanding some of the nonpsychopathological factors that potentially link depressive symptoms to suicide risk and that might themselves contribute to suicidality, even when controlling for depressive symptoms.