In this research, a combination of both quantitative and qualitative approaches is used to identify different market segments in the education industry. To solve the research problem, an exploratory approach to data mining is used and, using a series of interviews with experts, the factors affecting segmentation are identified. Then, using the…

The authors used a clustering technique to analyze business course choices made by students who completed an individualized degree in a large, urban, public university. They looked for patterns to answer the research question, "What can we learn from students' choices to inform the curricular redesign process in business programs?" The…

Although the importance of primary schools in the long term is of interest in educational effectiveness research, few studies have examined the long-term effects of schools over the past decades. In the present study, long-term effects of primary schools on the educational positions of students 2 and 4 years after starting secondary education are…

Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…

Multilevel models are often used to evaluate hypotheses about relations among constructs when data are nested within clusters (Raudenbush & Bryk, 2002), although alternative approaches are available when analyzing nested data (Binder & Roberts, 2003; Sterba, 2009). The overarching goal of this article is to suggest when it is appropriate…

Definitions of rurality in education research are inconsistent, making generalization across studies difficult at best. We review published research in rural education between 2005 and 2015 (n = 17) and characterize the way each defined rural. A common technique for classifying rural schools is the National Center for Educational Statistics (NCES)…

Gamification of education is a recent trend, and early experiments showed promising results. Students seem not only to perform better, but also to participate more and to feel more engaged with gamified learning. However, little is known regarding how different students are affected by gamification and how their learning experience may vary. In…

The objective of this study is to examine the social profile of students who are enrolled in Basic General Education in Ecuador. Both a descriptive and multidimensional statistical analysis was carried out based on the data provided by the National Survey of Employment, Unemployment and Underemployment in 2015. The descriptive analysis shows the…

The authors compared Web usability definitions, collected from library professionals at academic institutions of the Association of Research Libraries (ARL) through online surveys in 2007 and 2012, to determine whether library practitioners' perspectives had altered as information technologies evolved during this time. The authors applied three…

Extension professionals often assess community needs to determine programs and target audiences. Data can be collected through surveys, focus group and individual interviews, meta-analysis, systematic observation, and other methods. Knowledge gaps are identified, and programs are designed to resolve the deficiencies. However, do Extension…

The "p"-median clustering model represents a combinatorial approach to partition data sets into disjoint, nonhierarchical groups. Object classes are constructed around "exemplars", that is, manifest objects in the data set, with the remaining instances assigned to their closest cluster centers. Effective, state-of-the-art implementations of…

This study analyzes nationally-representative quantitative data from the first (2002) and second (2004) waves of the Educational Longitudinal Study to examine the relationship between Latina/o secondary school students' degree of English-language proficiency (ELP), mathematics course-taking measures, and 12th grade mathematics achievement.…

As the United States becomes more racially and ethnically diverse and draws more students from across the globe, more representative data are needed to understand at-risk and underrepresented populations in higher education, particularly in the science, technology, engineering, and mathematics (STEM) fields. The authors argue that the current…

New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…

This article discusses the effect of clustering on statistical tests conducted with school environment data. Because most school environment studies involve the collection of data from teachers nested within schools, the hierarchical nature to these data cannot be ignored. In particular, this article considers the influence of intraschool…