Tourism Market Segmentation using Big Data Approach: Where is the Next Non-Stop Destination?

Non-stop flights to a destination could reduce travel cost and increase visitor volumes. However, the airlines have to attract enough passengers to make those flights cost-effective. The identification of the next non-stop flying destination is one of the responsibilities of a Destination Marketing Organization (DMO). This paper develops a comprehensive model that encompasses the purchase funnel theory and the gravity model, which would help identify the potential markets for the next direct flight. Furthermore, the web traffic at the destination’s Convention and Visitors Bureau (CVB) is used as a proxy for each origin’s interest for the destination. This paper calculates the region’s potential to fly using multiple gravity models and compares actual visitors to the regions’ interest to the destination to find the most prospective markets. Theoretical background for the model and empirical evidence using the actual data of Charleston, South Carolina are provided for more thorough investigation

Student and Classroom Variables Improving Student Engagement in Mathematics

2004The study explored what student and classroom variables affected student engagement in mathematics. Since students were nested within a classroom, hierarchical linear modeling (HLM) was employed Jor the analysis. The results represented that students' gender, SES, and prior achievement made differences in student engagement. Teachers' years oj experience, certification, and degree had direct and indirect effects on student engagement level. Small size class had positive effects on student engagement, and content coverage also increased student engagement. Authentic instruction reduced the gender gap oj student engagement

School Effects Analysis on Science High School

2009The purpose of this study was to see if science high school has effects on improving student's science achievement. The research question was from recent doubt about effectiveness of Korean education on science talented youth. To answer the research question, science achievement scores of general and science high school students were compared under the control of variables both at student and at school level. HLM(Hierarchical Linear Modeling) was employed for analysis. The results showed that students in science high schools showed higher numbers than those in general high schools in both raw scores and scores after consideration of controlling variables. Interests in science positively predicted achievement scores regardless of school type. The number of mother's schooling years and SES scores at student-level, and the school mean SES and school location at school-level were the significant variables that predicted achievement scores