An exploratory study of pre-service teachers’ perceptions of technological pedagogical content knowledge of digital games

This study investigated pre-service teachers’ perceptions of technological pedagogical content knowledge of digital games (TPACK-G), the correlation of TPACK-G constructs, and the relation of TPACK-G to personal factors and levels of motivation and self-efficacy. Participants were 96 pre-service teachers from a university in the northeastern United States. Data were collected using online surveys. Quantitative approaches were performed to analyze the data. Results indicated that game content knowledge (GCK) and game pedagogical knowledge (GPK) significantly predicted pre-service teachers’ game pedagogical content knowledge (GPCK), with GPK being the strongest predictor. Pre-service teachers with high levels of motivation or self-efficacy for digital game integration had significantly better TPACK-G than those with low levels. Personal factors, including gender and prior experiences with digital games, were found to be influential to pre-service teachers’ TPACK-G. This study adds to the understanding of the application of the TPACK model in the context of digital game use for pre-service teachers

African American Students’ Academic and Web Programming Self-Efficacy, Learning Performance, and Perceptions towards Computer Programming in Web Design Courses

Computer programming has been included in computer literacy education in many countries in the last decade. This study examined the effects of gender and the prior programming experience of computer programming on academic and web programming self-efficacy and learning performance in the web design course among African American students, as well as their perceptions towards computer programming. This study’s 14-week web design course taught African American students multiple web programming languages, including HTML, CSS, and JavaScript, in order. A one-group pretest–posttest design was adopted in the experiment. The quantitative method was primarily used in data analysis. This study revealed that African American students’ academic and web programming self-efficacy significantly increased after the web design course. Most of the African American students’ perceptions of computer programming became positive after attending the web design course. This study also found that male African American students had a significantly higher level of web programming self-efficacy than female students before the web design course. Interestingly, this difference disappeared after the course. Additionally, both gender and prior experience in computer programming did not significantly affect students’ learning performance in the web design course. The findings of this study not only contribute to the understanding of the feasibility of teaching multiple programming languages in web programming courses for African American students, they also provide evidence of the positive influence of web programming on African American students’ perceptions of computer programming

Detecting Outliers in Data with Correlated Measures

Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect outliers so that models built from these datasets will not be skewed by outliers. In this paper, we propose a new outlier detection method that utilizes the correlations in the data (e.g., taxi trip distance vs. trip time). Different from existing outlier detection methods, we build a robust regression model that explicitly models the outliers and detects outliers simultaneously with the model fitting. We validate our approach on real-world datasets against methods specifically designed for each dataset as well as the state of the art outlier detectors. Our outlier detection method achieves better performances, demonstrating the robustness and generality of our method. Last, we report interesting case studies on some outliers that result from atypical events.Comment: 10 page