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.
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In this paper I investigate the differences between the 960 first year students at the department of Media, Information and Communication (MIC) of the University of Applied Sciences Amsterdam (UASA) in their use of new media and their background variables. With statistical test the various variables will be measured and compared with each other in order to give insight in the distribution of the variables among the students and possible relations between new media use and the students’ background. This study is part of a broader PhD research that investigates the relation between aspects of media literacy and students’ success. Therefore, the background variables that had proven to be factors of influence in student success by previous studies were also measured in this study. The use of new media was measured in the same survey amongst the students. The digital questionnaire was part of the career counselling course and mandatory for all students. With the insight of the distribution and relations between the different variables and the use of new media, this study will provide us a better few on the differences between the so-called Internet generation. Especially in the Netherlands, where 98% of all households with children have access to the Internet, a closer look into the differences could provide useful information for future research in digital divide and it’s shift from access to skills and differences in usage of the Internet.
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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.
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