Fatih Camadan

The new face of violence tendency

The present study examines the role of violence tendecy on cyberbullying perpetrations and their victims. Turkish adolescents N?=?286, 167 girls (58,4%) and 119 boys (41,6%) aged 15-18 years mean age of 17.6 years (df:0.78) were recruited from different high school types 101 (35.4%) of the participants were students in vocational high school, 88 (30.7%) anatolian high school and 97 (33.9%) public high school. Participants completed Revised Cyber Bullying Inventory (RCBI, Topcu & Erdur-Baker, 2010) and Violence Tendency Scale (VTS, Haskan & Yildirim, 2012). Collected data were analyzed Pearson correlation coefficients and simple regression. Pearson correlations revealed that violence tendency was positively related to cyberbullying perpetration (r?=?0.344, p?<?0.01). Also, violence tendency was positively related to cyber victimization (r?=?0.242, p?<?0.01). Also simple regressions revealed that violence tendency was significant individual predictors both cyberbullying perpetration and cyber victimization. There was a positive correlation between cyberbullies and violence tendency.There was a positive correlation between cyber victims and violence tendency.Cyberbullies and victims were significantly predicted by violence tendency.

Analysis of the Relation between Perfectionism and Negative Automatic Thoughts with Structural Equation Model

Abstract The aim of this study is to analyze the relations between perfectionism and negative automatic thoughts of teacher candidates. The participants of the study are composed of 243 teacher candidates who still continue their education in their 1., 2., 3., 4. grade in the School Teaching Programme of the Faculty of Education of Recep Tayyip Erdogan University in the academic year of 2011-2012 in Turkey. In order to determine the perfectionism feature of the teacher candidates “Positive and Negative Perfectionism Scale” is used and in order to determine their negative automatic thoughts “Automatic Thoughts Scale” is used. In the analysis of the data obtained, Pearson moments multiplication correlation coefficient is calculated and Structural Equation Modelling is practiced. In the consequence of the research, it is seen that positive perfectionism thoughts of teacher candidates affect negative automatic thoughts significantly in a negative way. In addition to that, it is seen that negative perfectionism features of the teacher candidates affect negative automatic thoughts in a positive way. In the light of these consequences, some suggestions have been made for this practice and for the resea rches to be conducted in the future.

How teachers’ personality affect on their behavioral intention to use tablet PC

Purpose The purpose of this paper is to explore the effects of personality traits on teachers’ technology acceptance. Design/methodology/approach To this end, a demographic information survey, Five-Factor inventory, and technology acceptance measure were used for data collection. The data were analyzed via structural equation modeling. Findings According to the findings, conscientiousness has no significant effect on behavioral intention (BI) whereas openness and extraversion have positive indirect effects. On the other hand, agreeableness and neuroticism have negative indirect effects on BI. It was seen that the developed model explains 70 percent of BI, 77 percent of attitude toward use, 42 percent of perceived usefulness, 64 percent of perceived ease of use, and 15 percent of computer self-efficacy (CSE). It was revealed that CSE mediates the indirect effects of different personality traits on these variables. Practical implications In this regard, highlighting the conveniences to be brought by using Tablet PCs and equipping teachers with competencies to use relevant technologies during teacher training and in-service training may be effective in developing BI to use Tablet PCs among teachers. Originality/value The present study is different from previous studies in that it examines more than one technology through more than one measurement, takes personality as the predictor of technology use tendencies, and focuses on causal relationships between technology use and personality.