Clustering of educational data allows similar students to be grouped, in either crisp or fuzzy sets, based on their similarities. Standard approaches are well suited to identifying common student behaviors; however, by design, they put much less emphasis on less common behaviors or outliers. The approach presented in this paper employs fuzzing…

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…

Post-secondary education is expected to substantially contribute to the cognitive growth and professional achievement of students studying science, technology, engineering, and mathematics (STEM). Yet, there is limited understanding of how students studying STEM develop a professional identity. We used the lens of self-authorship to develop a…

User clustering algorithms have been introduced to analyze users' learning behaviors and help to provide personalized learning guides in traditional Web-based learning systems. However, the explicit and implicit coupled interactions, which means the correlations between user attributes generated from learning actions, are not considered in these…

Today Learning Management Systems (LMS) have become an integral part of learning mechanism of both learning institutes and industry. A Learning Object (LO) can be one of the atomic components of LMS. A large amount of research is conducted into identifying benchmarks for creating Learning Objects. Some of the major concerns associated with LO are…

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…

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 paper focuses on firm behaviour within business clusters and cluster-type environments. Using survey data to distinguish cluster members by the perceived degree of participation, the authors categorize them as leading, proactive or reactive players. They then examine the clustering behaviour of each of these categories. They find that (a)…

This explorative data mining project used distance based clustering algorithm to study 3 indicators, called OIndex, of student behavioral data and stabilized at a 6-cluster scenario following an exhaustive explorative study of 4, 5, and 6 cluster scenarios produced by K-Means and TwoStep algorithms. Using principles in data mining, the study…

A study was conducted to refine and coordinate occupational analysis, job performance aids, and elements of the instructional systems development process for task specific Air Force maintenance training. Techniques for task identification and analysis (TI & A) and data gathering techniques for occupational analysis were related. While TI &…

This paper describes the practical steps employed in controlling interview data generated from a research project investigating teachers' perspectives on the worth of a sex equity educational demonstration project. These perspectives were then considered as value frameworks that might be used to formulate and interpret a naturalistic-responsive…