Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion of each of their first six terms. The study utilized exploratory and confirmatory techniques to reduce an initial pool of more than 50 potential predictor variables to a parsimonious final BN with only four predictor variables. The average in-sample classification accuracy rate for the model was 80% (Cohen's κ = 53%). The model was shown to be generalizable across samples with an average out-of-sample classification accuracy rate of 78% (Cohen's κ = 49%). The classification rates for the BN were also found to be superior to the classification rates produced by an analog frequentist discrete-time survival analysis model.

The study examined how ATFIND, Mantel-Haenszel, SIBTEST, and Crossing SIBTEST function when items in the dataset are modelled to differentially advantage a lower ability focal group over a higher ability reference group. The primary purpose of the study was to examine ATFIND's usefulness as a valid subtest selection tool, but it also explored the influence of DIF items, item difficulty, and presence of multiple examinee populations with different ability distributions on both its selection of the assessment test (AT) and partitioning test (PT) lists and on all three differential item functioning (DIF) analysis procedures. The results of SIBTEST were also combined with those of Crossing SIBTEST, as might be done in practice.

ATFIND was found to be a less-than-effective matching subtest selection tool with DIF items that are modelled unidimensionally. If an item was modelled with uniform DIF or if it had a referent difficulty parameter in the Medium range, it was found to be selected slightly more often for the AT List than the PT List. These trends were seen to increase as sample size increased. All three DIF analyses, and the combined SIBTEST and Crossing SIBTEST, generally were found to perform less well as DIF contaminated the matching subtest, as well as when DIF was modelled less severely or when the focal group ability was skewed. While the combined SIBTEST and Crossing SIBTEST was found to have the highest power among the DIF analyses, it also was found to have Type I error rates that were sometimes extremely high.

ABSTRACT The phenomenon of cyberbullying has captured the attention of educators and researchers alike as it has been associated with multiple aversive outcomes including suicide. Young people today have easy access to computer mediated communication (CMC) and frequently use it to harass one another -- a practice that many researchers have equated to cyberbullying. However, there is great disagreement among researchers whether intentional harmful actions carried out by way of CMC constitute cyberbullying, and some authors have argued that "cyber-aggression" is a more accurate term to describe this phenomenon. Disagreement in terms of cyberbullying's definition and methodological inconsistencies including choice of questionnaire items has resulted in highly variable results across cyberbullying studies. Researchers are in agreement however, that cyber and traditional forms of aggression are closely related phenomena, and have suggested that they may be extensions of one another. This research developed a comprehensive set of items to span cyber-aggression's content domain in order to 1) fully address all types of cyber-aggression, and 2) assess the interrelated nature of cyber and traditional aggression. These items were administered to 553 middle school students located in a central Illinois school district. Results from confirmatory factor analyses suggested that cyber-aggression is best conceptualized as integrated with traditional aggression, and that cyber and traditional aggression share two dimensions: direct-verbal and relational aggression. Additionally, results indicated that all forms of aggression are a function of general aggressive tendencies. This research identified two synthesized models combining cyber and traditional aggression into a shared framework that demonstrated excellent fit to the item data.

The present research study investigated the effects of 8 versions of a computer-based vocabulary learning program on receptive and productive knowledge levels of college students. The participants were 106 male and 103 female Korean EFL students from Kyungsung University and Kwandong University in Korea. Students who participated in versions of the vocabulary learning program with target-word based sentences as well as definitions tended to perform better on receptive and productive vocabulary assessments than those who participated in versions of the program with definitions of words only. Furthermore, results indicated that the difference in receptive scores from immediately after the program to one week later showed a higher drop-rate than the difference in productive scores. In addition, female learners performed receptively better than male learners in post and one-week delayed tests, but significant gender difference failed to occur for the productivity measure. Overall, these results emphasized the importance of productive vocabulary knowledge for better retention of English vocabulary words.