Tinto’s integration theory has highly influenced research on student success in Europe and America. However, due to the complexity of the theory and the enormous amount of variables, the theory is not suitable for use in regular evaluations in higher education.By including only the best-proven predictive variables, I reduced the amount of variables from Tinto’s theory, avoiding the capitalization of chance and establishing a more easy to use model for teachers and management. The latent variable ‘satisfaction’ was built by using a fraction of the original manifest variables. It was tested, using principal component analysis, in a previous study to prove a good fit of the model. In this paper I focus on the role of background variables (gender, ethnicity, previous education and living situation), to measure their possible influence. A multi-group comparison (X2 difference test) in SPSS AMOS is conducted and path analysis is done to uncover differences on individual paths between the variables.This paper is part of my PhD research, wherein I investigate the possible influence of the use of social media by first year students in higher education.
The prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients’ responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the nonphysical origins of HF.
As part of my PhD research, I investigate the influence of the use of social media by first year students in higher education. In this research I have lessened the amount of variables, from Tinto’s theory, by including only the best-proven predictive variables, based on previous studies. Hereby, avoiding the capitalization of chance and a more easy to use model for teachers and management has been built. The latent variable ‘satisfaction’ is constructed by using just a fraction of the original manifest variables and tested using principal component analysis to proof the model can be simplified. Furthermore, I enriched the model with the use of social media, in particular Facebook, to better suit students’ contemporary society in the developed world. With principal analysis on Facebook usage, I measured the purpose of Facebook use (information, education, social and leisure) and the use of different pages amongst students. This provided different integration/engagement components, which are also included in the simplified model. For the principal component-analysis, Cronbach’s alpha and Guttman’s lambda-2 showed internal consistency and reliability. SPSS AMOS was used for testing the fit of the model and showed reasonable values for the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA). This study will compare different background variables with the model to uncover the possible influences upon student success, engagement/satisfaction and social media use. Ultimately this paper will provide a better insight into what kind of influence social media can have upon student success.
